Period and Glitch Reduction Via Clock Skew Scheduling, Delay Padding and GlitchLess by Xiao Dong B.A.Sc., The University of British Columbia, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Applied Science in The Faculty of Graduate Studies (Electrical and Computer Engineering) The University of British Columbia (Vancouver) September, 2009 c© Xiao Dong 2009 Abstract This thesis describes PGR, an architectural technique to reduce dynamic power via a glitch reduction strategy named GlitchLess, or to improve performance via clock skew scheduling (CSS) and delay padding (DP). It is integrated into VPR 5.0, and is invoked after the routing stage. Programmable delay elements (PDEs) are used as a novel architecture modification to insert delay on flip-flop (FF) clock inputs, enabling all optimization steps to share it, avoiding multiple architecture modifications. This thesis investigates the trade- off between power and performance, and finding an appropriate compromise considering process variation and timing uncertainties. To facilitate realistic power estimates, a popular activity estimator, ACE, is modified with a new model to estimate glitching power, taking into account the analog behavior of glitch pulse width reduction as it travels along FPGA routing tracks. We show that the original glitch estimation method can underestimate glitching power by up to 48%, and overestimate by up to 15%. In terms of performance, an average of 15% speedup can be achieved via CSS alone, or up to 37% for individual circuits. Although delay padding only benefits a few circuits, the average improvement of those circuits is an additional 10% of the original period, or up to 23% for individual circuits. In addition, GlitchLess is performed on both the original VPR and post-CSS solutions. On average, 16% of glitching power can be eliminated, or up to 63% for individual circuits. ii Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Synchronous Circuit and Clock Skew . . . . . . . . . . . . . . . . . . . . . 6 2.2 Clock Skew Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Solving CSS With Graph Theory . . . . . . . . . . . . . . . . . . . 10 2.2.2 Solving CSS with Permissible Range . . . . . . . . . . . . . . . . . . 12 2.2.3 Exact CSS Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 iii Contents 2.3 CSS for FPGAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 FPGA Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 FPGA CAD Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.3 Clock Skew Scheduling Techniques for FPGAs . . . . . . . . . . . . 17 2.4 Delay Padding (DP) for CSS . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.1 Delay Insertion using Linear Programming . . . . . . . . . . . . . . 20 2.4.2 Race Condition Aware (RCA) Clock Skew Scheduling . . . . . . . . 21 2.5 Activity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.2 Simulation-Based Activity Estimation . . . . . . . . . . . . . . . . . 23 2.6 Dynamic Power Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.7 Glitch Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3 Glitch Generation Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.1 Introduction and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Cadence Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Glitch Binning Algorithm Description . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 ACE Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.2 Event Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.3 ACE Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4 Power Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4 Architecture and Algorithm Overview . . . . . . . . . . . . . . . . . . . . 38 4.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.1.1 Architecture for CSS and Delay Padding . . . . . . . . . . . . . . . 38 iv Contents 4.1.2 Architecture for Glitch Reduction . . . . . . . . . . . . . . . . . . . 40 4.1.3 Alternative PDE-Sharing Architecture . . . . . . . . . . . . . . . . 40 4.1.4 Interaction between CSS and GlitchLess . . . . . . . . . . . . . . . 40 4.2 Algorithm Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3 PGR Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3.1 Node Building Stage . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3.2 Node Attribute Calculation . . . . . . . . . . . . . . . . . . . . . . . 46 5 Detailed Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.1 CSS and DP Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.1.2 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.1.3 Variable PDE-per-CLB Restriction . . . . . . . . . . . . . . . . . . 57 5.1.4 Interface with GlitchLess . . . . . . . . . . . . . . . . . . . . . . . . 58 5.2 Glitch Reduction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.2.2 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . 60 6 Experimental Results and Discussion . . . . . . . . . . . . . . . . . . . . . 62 6.1 CSS-Only Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.1.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.1.2 Power Overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.1.3 Area Overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.2 GlitchLess Only Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.3 GlitchLess Savings After CSS+DP+GL Run . . . . . . . . . . . . . . . . . 78 6.4 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 v Contents 7 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.2.1 CSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 7.2.2 Glitch Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 vi List of Tables 3.1 Glitch Power (Pop) of Original ACE and ACE with Binning . . . . . . . . . 36 4.1 Sequential Circuit Characteristics and Glitching Power . . . . . . . . . . . . 42 5.1 Feature Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.1 CSS and DP Results, % of Original Period, k=4 . . . . . . . . . . . . . . . 64 6.2 CSS and DP Performance, % of Original Period, k=6 . . . . . . . . . . . . 65 6.3 Power after CSS and DP, % of Original Dynamic Power, k = 4 . . . . . . . 68 6.4 Power after CSS and DP, % of Original Dynamic Power, k = 6 . . . . . . . 68 6.5 Dynamic Power after GlitchLess . . . . . . . . . . . . . . . . . . . . . . . . 77 6.6 Dynamic Power after Full Run, Excluding CSS+DP PDE Overhead . . . . 79 vii List of Figures 2.1 Example Synchronous Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Intentional Clock Skew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Binary Search for Optimum Period (from [11]) . . . . . . . . . . . . . . . . 11 2.4 Basic Logic Element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 Configurable Logic Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.6 An Example FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.7 CSS with Skewed Global Clocks . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.8 Global H-tree with Ribs for Local Routing . . . . . . . . . . . . . . . . . . . 19 2.9 PDE Insertion at Local Ribs (from [31]) . . . . . . . . . . . . . . . . . . . . 20 2.10 RCA Algorithm (from [18]) . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.11 Detailed Relax Hold Algorithm(GOCSS) (from [18]) . . . . . . . . . . . . . . 22 2.12 Main ACE Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.13 ACE: propagate events(circuit) . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.14 Architectural Modification for GlitchLess . . . . . . . . . . . . . . . . . . . 26 2.15 GlitchLess: calc needed delays(circuit) (from [19]) . . . . . . . . . . . . . . 27 2.16 GlitchLess: config LUT delays(circuit, min in, max in, num in) (from [19]) 28 3.1 Length-4 Wire Stage Overall View . . . . . . . . . . . . . . . . . . . . . . . 30 3.2 Fragment Detail View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 viii List of Figures 3.3 Segment Length to Power Lookup (65nm Technology) . . . . . . . . . . . . 31 3.4 Glitch Filtering Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.5 Modified ACE Routine: propagate events(circuit) . . . . . . . . . . . . . . . 34 3.6 Glitch Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1 Unified Architecture Modification . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2 PDE Sharing Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3 Top Level Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.4 PGR Top Level Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5 BLE in Sequential Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.6 Calculating Intra-CLB Capacitance . . . . . . . . . . . . . . . . . . . . . . . 47 5.1 CSS and Delay Padding Flow Chart . . . . . . . . . . . . . . . . . . . . . . 52 5.2 CSS and Delay Padding Algorithm . . . . . . . . . . . . . . . . . . . . . . . 53 5.3 Detailed Delay Padding Algorithm, pad delay() . . . . . . . . . . . . . . . . 56 5.4 Adding Skew to Circuit Timing . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.5 Glitch Reduction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.1 Skew Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.2 Glitch Power Reduction, 1 PDE per FF . . . . . . . . . . . . . . . . . . . . 71 6.3 Glitch Power Reduction, PDE Sharing . . . . . . . . . . . . . . . . . . . . . 72 6.4 Power Plot Without PDE Overhead . . . . . . . . . . . . . . . . . . . . . . 74 6.5 Power Plot With PDE Overhead . . . . . . . . . . . . . . . . . . . . . . . . 75 6.6 PDE Usage for GlitchLess . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 ix Acknowledgements First of all, I would like to thank my supervior Guy Lemieux, for his unwaivering support and guidence for two years. Without him, I would not be writing this thesis. I am very grateful for his time and patience. Thanks to Roozbeh Mehrabadi of the SOC lab for his timely technical support when CAD tools stray from their expected behavior. I would also like to thank my lab-mates, Usman Ahmed, Scott Chin, Darius Chiu, Chris Chou, David Grant, Julien Lamoureux, Paul Teehan, and Mark Yamashita for their help when I was stuck on various stages of my research. My last but not the least thanks goes to my parents for standing by me all these years, for their encouragement when I was down, for helping me to be a successful student, and for teaching me to be a better person. x Chapter 1 Introduction 1.1 Motivation Power and performance are two very important issues in FPGA design. FPGA applications typically consume more power per operation, and run at slower speeds than their ASIC counterparts, due to circuitry needed for programmability. There is much research effort addressing these two topics. On the performance front, two popular techniques are retiming and clock skew scheduling (CSS). The former method changes the positions of sequential elements (SEs) to shorten the effective critical path while maintaining functionality [21]. This work has also been applied to FPGAs [9, 25, 29]. One disadvantage of retiming is that the skew achievable may not be finely adjustable because the number of locations a SE can be placed is limited. Instead, CSS achieves period reduction by assigning intentional clock skews to SEs [11, 14] rather than moving them physically, and has also been applied to FPGAs [28, 30, 36]. Compared to retiming, the skews required by CSS may be realized using programmable delay elements (PDEs) that can be finely adjusted to provide many levels of quantized skew. Total dynamic power consumption is significant for FPGAs due to large capacitive loading on the programmable interconnect. Recent advances in process technology have seen a decreasing trend in the rate of increase of dynamic power versus static power. However, total dynamic power still accounts for about 50% of total power [5]. In this 1 Chapter 1. Introduction thesis, our use of the term “dynamic power” shall exclude clock network power; where appropriate, it will be considered separately. Dynamic power arises from two kinds of logic transitions produced by combinational logic building blocks in FPGAs called look-up tables (LUTs): functional and glitch. The former causes the data to be different at the end of a clock period, a result of user logic functions. The latter results from input data signals arriving at different times during the period, causing the output to fluctuate before settling down. Several existing examples to reduce glitching power include techniques at the architecture level [19], or at the CAD level during technology mapping [7] and routing [12]. 1.2 Objectives The purpose of this thesis is to achieve performance and power optimization with a single architecture change. This change increases FPGA area, but it does not alter the existing place-and-route algorithms and it makes only small netlist changes after routing is com- plete. This approach is called PGR, for period and glitch reductions. CSS and GlitchLess [19] are chosen as a basis of this work because these two methods can be applied to the proposed architecture. The algorithm will try to reduce period and power within the lim- itations of the architecture and the final routing solution. The PDE proposed in [19] is used here to provide discrete delays. To reduce period to the lowest possible value without violating timing requirements, the CSS algorithm iteratively determines if there exists a set of quantized skews that can satisfy a given clock period, based on a set of constraints given by the properties of the circuit. Once the improved period has been determined, a technique called delay padding (DP) is used to relax the constraints used when solving the CSS problem, possibly achieving even further period reduction. In addition, process variation can cause signals to arrive earlier 2 Chapter 1. Introduction or later than desired, and PGR takes this into account and allocates extra timing margins. With a traditional zero-skew clock network, the departure time of all SEs is synchro- nized to the active clock edge. Skews on SE clocks change the departure times by varied amounts, causing downstream nodes’ arrival times to change. This affects the amount of glitching present in a circuit, usually increasing it. In either case, an accurate tool is needed to determine the amount of glitching activity on each node. A node is either a SE, or a combinational LUT. An existing tool called ACE [20] is one such tool. ACE uses a threshold to determine whether a glitch does not propagate at all or propagates indefi- nitely until the next node. One goal of this work is to model the analog behavior of the gradual decrease in width of a narrow glitch as it travels along FPGA interconnect, and to determine the relationship between glitch pulse width and power consumption. More accurate glitch estimation leads to GlitchLess making better decisions on which node to reduce glitches, based on the amount of glitching power its output produces. In this thesis, the original GlitchLess concept is used with a different implementation. Previously, delay elements added to node inputs needed to be very precise to eliminate glitching. This can be difficult with increasing process variations, which adds delay un- certainty. The new approach is much more resistant to variation because it prevents the output from transitioning until after the last expected input has arrived. One final objective is to restrict the number of PDEs available in the architecture in an effort to save area. The PGR algorithm will try to reduce the number of PDEs used and the impact on optimization results via a two-pass approach. The first pass will assume every SE has access to a PDE. The second pass arranges SEs to share a reduced number of PDEs, and the sharing schedule is decided based on the results from the first pass. 3 Chapter 1. Introduction 1.3 Contributions This thesis makes the following contributions summarized from the last section: 1. A unified architecture change, shared by CSS, delay padding and glitch reduction, is proposed which avoids the need for multiple architecture modifications. For glitch reduction, GlitchLess is applied using a different implementation than previous work. 2. Integrated delay padding scheme with CSS further optimizes performance. Past work [16, 18, 22, 33] uses either LP or graph algorithms to improve CSS. However, these techniques apply only to ASICs, and assume padded delays are continuous. However, FPGAs must use PDEs, and a PDE can only provide discrete delays. We adapt the algorithms to use discrete delays as well as margin for process variation. 3. PGR uses the same physically realizable architectural change to reduce power and increase performance. CSS, delay padding and glitch reduction techniques are com- bined with VPR 5.0 [23] into a single executable. This is important for getting a final result that considers both delay and power at the same time. 4. An improvement on vector based activity estimation [20] is proposed, taking into account the analog behavior of glitch pulses that travel along routing tracks. The resulting glitching power estimation is therefore more realistic. The central theme of this work highlights the major difference of this work: previous related research has focused purely on either performance or power. Our work shows performance optimization adds to power, while 100% glitch reduction is not possible without impacting performance. Therefore it is important to achieve an appropriate compromise between the two. Furthermore, better PDE designs are motivated by putting PDE power overhead in perspective with total dynamic power consumption before and after glitch reduction: while 4 Chapter 1. Introduction there is potential for good savings, a power-efficient PDE is crucial to the attractiveness of glitch reduction. Part of the work done in this thesis has been accepted as a conference paper [13]. 1.4 Thesis Organization The rest of the thesis is organized as follows. Chapter 2 introduces basic concepts, includ- ing a brief overview of FPGA architecture, CSS, DP and activity estimation techniques, and GlitchLess. Chapter 3 describes the modifications to ACE. Chapter 4 describes the architecture changes and gives a brief overview of the algorithm. A detailed algorithm description is presented in Chapter 5. Chapter 6 gives detailed results and discussion, and Chapter 7 concludes the thesis and presents possible future work. 5 Chapter 2 Background This chapter first presents the basics of a synchronous circuit, followed by a discussion of the clock skew scheduling (CSS) technique to improve performance. Past solutions and optimizations of CSS applied to Field-Programmable Gate Arrays (FPGAs) are described in detail. Delay padding, a useful extension of CSS that currently applies only to ASICs, is presented. The second part of the chapter presents concepts related to dynamic power reduction via glitch elimination. Discussion on activity estimation, power calculation and GlitchLess [19] is presented. 2.1 Synchronous Circuit and Clock Skew A synchronous circuit is made up of blocks of combinational logic in between pairs of sequential elements (SEs) (denoted by Ri) connected by a common, periodic clock source. An example is shown in Figure 2.1. Each cycle, an incoming clock edge triggers Rj , which releases a data signal that travels through a block of combinational logic, and the computation result is stored in Rk. These two SEs and the combinational path form a local data path, or a pipeline stage. The entire circuit may be referred to as a pipeline, with each incoming data signal moving from one stage of the pipeline to the next according to the clock. In research literature, a common type of SE used is the positive clock edge triggered D flip flop (FF). Another type of SE is a flow-through latch, where changes at 6 Chapter 2. Background Figure 2.1: Example Synchronous Circuit the input are immediately transferred to the output for the duration of the duty cycle. To determine the duty cycle of the latch’s clock input, more timing constraints are needed to satisfy both clock edges of the duty cycle, resulting in a more complex problem. Therefore, FFs will be used throughout the thesis. An important figure of merit of synchronous circuitry is the maximum operating fre- quency or, equivalently, the minimum clock period. A smaller period in between FFs requires a smaller data delay from FF output to input. Theoretically, the minimum period (P ) is the maximum local data path delay (Dmax) in the circuit plus the setup time re- quired for stable register operation (Tsetup). This is known as a setup-time constraint (Eq. 2.1). P ≥ Tsetup +Dmax (2.1) A violation of Eq. 2.1 will result in a zero-clocking condition [14] or a setup-time violation, where data from FFi reaches FFj too late relative to the next clock edge, and no new data is clocked to the next pipeline stage. The clock is distributed in the circuit as a tree network, and it can limit the theoretical performance of synchronous circuits. It is often the largest net in the circuit, connecting to every FF in the circuit. Therefore, a clock signal may have to travel a long distance to reach FFs that are far away. Consequently, the clock’s load capacitance due to wire length is often the greatest of all nets. These factors can cause a difference in the arrival time, or clock skew, of the clock signal to FFs at different parts of the circuit [15, 27]. Usually, 7 Chapter 2. Background circuit designers take great efforts to reduce this clock skew as much as possible. After accounting for skew at individual FFs, the resulting setup-time constraint is: Tj − Ti ≥ Tsetup +Dmax(i, j)− P (2.2) where Ti and Tj are clock arrival time at FFi and FFj , respectively. Dmax(i, j) is the maximum combinational delay between FFi and FFj . Clock skew gives rise to another possible circuit failure called the double-clocking condition, which is a type of hold-time violation: Ti − Tj ≥ Thold −Dmin(i, j) (2.3) where Thold is the register’s hold time, and Dmin(i, j) is the minimum combinational delay between FFi and FFj . Hold time violation occurs when the next data reaches FFj too early relative to its next clock edge (due to clock skew), thereby overwriting the old data it was to capture. This will result in the old and new data being clocked into a stage during the same clock cycle, hence the name double-clocking [14]. Finally, process variation also makes it difficult to control device parameters such as channel length, width, dopant concentrations and gate thickness. Therefore, the threshold voltage and delay of logic gates may vary from chip to chip [8] or even between different gates on the same chip. This adds uncertainty into the above setup/hold time constraint equations. Often, this uncertainty is modeled by adding a timing margin (also called a guard band) M to the equations. 8 Chapter 2. Background 2.2 Clock Skew Scheduling In 1990, Fishburn [14] proposed to use clock skew as a resource for improving performance, instead of treating it as an unavoidable burden. For example, consider the circuit in Figure 2.2. Assuming zero setup/hold times, a zero-skew clock network means the circuit has a minimum period of 14ns. If a skew of 4ns is applied to FFB, the circuit is able to operate at a minimum period of 10ns. This effect can be viewed as time borrowing by shortening the effective delay of long paths, at the expense of increased delay for short paths. Indeed, the path from FFB to FFC now has an effective delay of 10ns from the clock edge. Figure 2.2: Intentional Clock Skew The optimization problem is: Minimize P , subject to: |Ti| < P Tj − Ti ≥ Tsetup +Dmax(i, j)− P Ti − Tj ≥ Thold −Dmin(i, j) This is a Linear Programming (LP) problem, and can be solved by an LP solver. An issue with this scheme is process variation. In Figure 2.2, a 10ns period puts both 9 Chapter 2. Background local paths on the verge of violating the setup time constraint. The uncertainty in gate delays and clock skews can cause zero clocking to occur. To fix this, a fixed amount of slack, or safety margin, is added to all local paths, at the expense of increased P [11]. The final LP problem is as follows: Given P, maximize M, subject to: |Ti| < P Tj − Ti ≥ Tsetup +Dmax(i, j)− P +M Ti − Tj ≥ Thold −Dmin(i, j) +M (2.4) The safety margin compensates for process variation and allows Ti, Tj and the path delays to vary by M in total without violating the constraints. 2.2.1 Solving CSS With Graph Theory The CSS problem can be solved more efficiently using graph theory [10], and is demon- strated by [11]. In [10], a difference constraint is defined as a linear inequality in the form xj − xi ≤ b, where b is a constant. For a set of difference constraints given in Eq. 2.4, a directed graph G(V,E), called the constraint graph, may be constructed where vertex vi corresponds to Ti, and edge weights correspond to the right hand side of the constraint equations. It is given that the values of vi form a solution set for Eq. 2.4 if there are no negative weight cycles in G(V,E). To find the minimum achievable period, a binary search is performed between upper and lower bounds: 10 Chapter 2. Background Pmax = max q = (i, j) ∈ G(V,E){Tsetup +Dmax(i, j) +M} Pmin = max q = (i, j) ∈ G(V,E)({T + Thold +Dmax(i, j)−Dmin(i, j) + 2M} (2.5) where q is an edge in the constraint graph, Pmax is the largest local path delay in the circuit plus the safety margin, and Pmin is determined from equating the setup/hold constraints in Eq. 2.4 such that both are satisfied simultaneously. In each binary search iteration, the set of constraints in Eq. 2.4 is tested using graph theory. The goal is to determine if there exists a suitable clock schedule to satisfy a given P . A single-source shortest path algorithm can be used on G(V,E) to find a solution to each Ti such that Eq. 2.4 for each pair of FF is satisfied, and that no negative weight cycles occur. The Bellman-Ford algorithm [10] is a suitable algorithm for this problem. A virtual vertex vo is connected to primary input/output nodes to transform the graph into a single-source graph, and all other vertices contain Ti, the shortest-path weights from vo. Within the bound given by Eq. 2.5, the Bellman-Ford algorithm is used for each iteration of the binary search, until the bounds are ε apart from each other, as defined by the user. 1: while (Pmax − Pmin) > do 2: P = (Pmin + Pmax)/2 3: if G(V,E) has a positive cycle then 4: Pmax = P ; 5: else 6: Pmin = P ; 7: end if 8: end while Figure 2.3: Binary Search for Optimum Period (from [11]) 11 Chapter 2. Background 2.2.2 Solving CSS with Permissible Range Given a pair of setup/hold constraints for a local path, the permissible range is the range of values (Tskewij = Ti−Tj) for which the setup/hold constraints remain satisfied. Neves and Friedman [24] solves the CSS problem by using a binary search to determine the permissible range of all local paths, subject to user-specified minimum value. For reconvergent paths (two or more local paths with a common source and sink) or cycles (feedback loops that begin and end at the same FF), the intersection of the permissible ranges is taken to form the effective permissible range. Tskewij is then chosen to be the middle of the effective permissible range. This approach leaves as much safety margin as possible on either side of the chosen skew to tolerate the unknown process variation effects. 2.2.3 Exact CSS Solution The authors of [4, 32] state that the CSS problem can be solved by finding the minimum mean cycle in the constraint graph. The authors of [32] gives the complete algorithm description for the solution. A Z-cycle is defined as a cycle containing at least one hold-time constraint type edge. Finding the minimum period is equivalent to finding the minimum Z-cycle in the constraint graph. The work claims a polynomial runtime complexity. Furthermore, the authors of [4] point out that it is much more expensive to generate the constraint graph (referred to as the sequential graph in [4]) than it is to compute the minimum mean cycle. They proposed an algorithm that solves the CSS problem while extracting only part of the graph, starting at the most timing-critical region of the circuit. The work claims that the runtime is reduced to 5.8% of the original, and that only 20% of the sequential circuit needs to be extracted. In this thesis, the binary search method in [11] is used instead of the exact CSS solution method, since it is easier to adjust post-CSS skews to discrete delays (required by the 12 Chapter 2. Background programmable nature of FPGAs) in the Bellman-Ford framework, and it is also easier to implement. 2.3 CSS for FPGAs The discussion so far only considers CSS for ASICs. FPGA technology is reprogrammable alternative to ASICs. They allow fast turnaround time, and they are very popular for rapid prototyping and numerous other low-volume applications. This section presents a brief overview of the FPGA architecture and CAD design flow, followed by existing CSS techniques for FPGAs. 2.3.1 FPGA Architecture The basic building block of an FPGA is called a Basic Logic Element (BLE). Each BLE contains a k-input, single-output Look Up Table (LUT) and a FF as shown in Figure 2.4. The purpose of the LUT is to implement an arbitrary logic function of up to k inputs. If the multiplexer is selected to bypass the FF, the BLE will be used as combinational logic. Otherwise, the BLE will act like the end of a pipeline stage in sequential logic. BLEs are grouped together into logic clusters [6], which are also referred to as con- figurable logic blocks (CLB). A CLB may contain N BLEs grouped together as shown in Figure 2.4: Basic Logic Element 13 Chapter 2. Background Figure 2.5: Configurable Logic Block Figure 2.5, sharing I distinct inputs. A BLE inside the CLB can choose either one of the CLB inputs, or any feedback signal from one of the BLE outputs in the same cluster via the fast local routing. Circuit components that are closely connected with each other can take advantage of the fast local routing to improve overall speed [6, 35]. A generic FPGA is made of CLBs (L block) grouped together in a grid like fashion, shown in the top part of Figure 2.6. CLBs are surrounded by programmable routing fabric channels, which are illustrated in more detail in the bottom part of Figure 2.6. The versatility of an FPGA comes from the ability of every CLB being able to arbitrarily connect to any other CLB via the programmable switches (S block) and connections (C block). The number of wires in the routing channel is called the channel width. The components in a FPGA are also susceptible to process variation. However, circuits implemented in a FPGA operate at much slower frequency than ASICs, making the pro- 14 Chapter 2. Background Figure 2.6: An Example FPGA 15 Chapter 2. Background cess variation effect less significant. However, FPGAs are becoming faster with each new manufacturing process shrink, making process variation a more noticeable issue. In this thesis, unless otherwise mentioned, the following major architectural parameters are used: • k (LUT size): 4 and 6 • N (CLB size): 10 • I (Inputs per CLB): 22 for k=4 and 33 for k=6 • FC input (fraction of routing wires each CLB input pin can connect to): 0.2 • FC output (fraction of routing wires each CLB output pin can connect to): 0.1 • Segment Length (number of CLBs spanned by a routing wire): 4 • Switch Type: uni-directional buffered MUXes. • Rmetal (resistance of routing wire per CLB length): 56.122Ω [3] for 65nm technology, based on an estimated 125µm CLB length • Cmetal (capacitance of routing wire per CLB length): 21.13425fF [3] for 65nm tech- nology, 125µm CLB length • Other detailed information are obtained from the iFAR repository [2] 2.3.2 FPGA CAD Flow To transform a circuit design onto a FPGA, the first step, technology mapping, maps user designed logic gates into k-input LUTs and FFs. A clustering algorithm then packs these into CLBs with N BLEs. Closely connected LUTs are usually packed into the same CLB 16 Chapter 2. Background to take advantage of the fast local routing. The result of this step is a netlist file that describes which LUTs and FFs are inside each CLB. This file is used for the next step, placement, to map the CLBs onto physical locations on the FPGA chip. The VPR tool [6] is a very popular tool in academic research. VPR 5.0 is the latest version. It is used in this thesis and shall be simply referred to as VPR. It uses a placement technique called simulated annealing, which starts with a random placement CLBs scattered on the FPGA. Then, two random CLBs’ positions are swapped, the cost of the placement is recalculated, and the swap is kept if the cost is lower. If the swap causes a cost increase, it may also be kept depending on a probability (called the temperature of the anneal process) that slowly decreases with time. This process is repeated until the temperature reaches a low point specified by the user. During annealing, the placement cost is usually a function of critical path delay and the interconnect area the circuit requires. The last step is called routing, which connects CLBs together via the routing resource. Two common metrics optimized by the router are channel width and critical path delay. VPR uses an architecture file that contains information described in the previous section to do placement and routing. In this thesis, timing-driven placement and routing are used, with a fixed channel width of 104 as specified in the architecture files obtained from [2]. Clock skew scheduling is performed after routing, the last stage of the regular FPGA CAD flow. 2.3.3 Clock Skew Scheduling Techniques for FPGAs Singh and Brown [28] use multiple global clock lines (L in total) in the FPGA to distribute skews to FFs, as shown in Figure 2.7. The work distributes multiple copies of the same clock with precisely ranged phase shifts, generated by on-chip PLLs. The optimization algorithm is similar to that presented in [14], but it must select a set of L distinct discrete skew values to realize the best possible schedule. Therefore, a discrete version of the 17 Chapter 2. Background Bellman-Ford algorithm is used. Figure 2.7: CSS with Skewed Global Clocks An alternative approach by Yeh et al. [36] uses a single global H-tree with ribs on the H-tree for local routing as shown in Figure 2.8. The far right picture in Figure 2.8 shows the detailed local routing, where programmable delay elements (PDE) are inserted into branching points of the clock tree. Under this architecture, the clock signal goes through a trail of PDE nodes (from R to d in Figure 2.8) before arriving at each FF node. This levelized structure provides more choices for skew values than Singh and Brown’s approach [28]. Since the max amount of delay a PDE can provide is fixed, an additional constraint must be satisfied when solving the optimization problem: Υij ≤ sj − si ≤ Υij + ζi (2.6) Where Υij is the interconnect delay between two PDE nodes or between a FF node and a PDE node at the end of the trail that provides its clock, sjand si are clock arrival times of the two nodes, and ζi is the amount of delay provided by the PDE. For example, consider Figure 2.8. The clock arrival time of FF2 (si) is equal to the sum of PDE-d’s arrival time (sj), the delay it provides (ζd), and the delay of the wire (Υij) between it and FF2. 18 Chapter 2. Background Figure 2.8: Global H-tree with Ribs for Local Routing A third architecture uses the same spine and ribs clock network as [36], but inserts PDEs only at the local ribs [31]. Shown in Figure 2.9, this method produces 4 skewed version of the global clock for each row. In addition, a statistical timing model is used to express maximum and minimum path delays as Gaussian variables, and k is the user- defined uncertainty factor to account for path delay variations. Dmax(i, j) = µmax + k · σmax Dmin(i, j) = µmin − k · σmin (2.7) 2.4 Delay Padding (DP) for CSS The setup/hold constraints can limit the range of skews that can be assigned to SEs, and therefore the smallest obtainable period. In Eq. 2.2 and 2.3, larger Dmax and smaller Dmin 19 Chapter 2. Background Figure 2.9: PDE Insertion at Local Ribs (from [31]) will decrease the permissible range of assigned skews. Nothing can be done to decrease Dmax(i, j), but an increase in Dmin(i, j) will widen the permissible range, allowing skew assignment to be more flexible. This short-path optimization effectively reduces hold time violations, allowing a smaller period. This section will describe several existing approaches for delay padding for ASICs. To our knowledge, delay padding has not yet been applied to FPGAs. 2.4.1 Delay Insertion using Linear Programming Taskin and Kourtev [33] proposed to insert delays into signal paths. The authors show for reconvergent paths that are timing-critical, a smaller minimum period is achievable by decreasing the difference between the maximum and minimum path delays of each reconvergent path. A new set of setup/hold constraints are formulated in Eq. 2.8, where Dmax(i, j), Dmin(i, j) and IMij , Imij define uncertainty bounds for each reconvergent path from FFi to FFj , and the delay provided by the inserted delay element, respectively. 20 Chapter 2. Background Since there are three unknowns to solve for each constraint, Eq. 2.8 no longer forms a set of difference constraints that can be solved by graph theory, and LP is used assuming continuous skews and delays are available. Minimize P, subject to: IMij ≥ Imij Tj − Ti ≥ Tsetup +Dmax(i, j)− P + IMij Ti − Tj ≥ Thold −Dmin(i, j)− Imij (2.8) 2.4.2 Race Condition Aware (RCA) Clock Skew Scheduling Huang and Nieh [17] uses an iterative method to find a clock skew and delay padding solution. The overall algorithm is in Figure 2.10. 1: (GDEL, PRCA, SDEL) = Relax Hold(GOCSS); 2: (GINS) = Parameter Assign(GDEL, SDEL); 3: (GRCA, SRCA) = Parameter Minimization(GINS , PRCA); 4: return (GRCA, PRCA, SRCA); Figure 2.10: RCA Algorithm (from [18]) The first part of the overall algorithm, Relax Hold, is shown in Figure 2.11. The original circuit’s constraint graph GOCSS is the input. During iteration k, a set of skews (SD(k)) and a period (PD(k)) are determined using the binary search method in [11]. Then, the constraint graph is stripped of any critical hold-time edges (H-edges). A hold-time edge (essentially a hold-time constraint in Eq. 2.4), is a critical hold-time edge if both inequalities in Eq. 2.4 become equalities, forming a critical cycle. CSS is then performed again to determine a lower period, and the process is repeated until no further performance 21 Chapter 2. Background 1: k=0; GD(k) = GOCSS ; 2: derive SD(k) and PD(k) with respect to GD(k); 3: repeat 4: obtain GD(k+1) by deleting all the actual critical H-edges in GD(k) with respect to SD(k); 5: derive SD(k+1) and PD(k+1) with respect to GD(k+1); k++; 6: until (GD(k) == GD(k)); 7: GDEL = GD(k−1); PRCA = PD(k−1); SDEL = SD(k−1); 8: return (GDEL, PRCA, SDEL); Figure 2.11: Detailed Relax Hold Algorithm(GOCSS) (from [18]) optimization is obtainable. At the end of Relax Hold, the optimum set of skews (SDEL), period (PRCA) and constraint graph excluding deleted critical hold-time edges (GDEL) are produced. During Parameter Assign, each deleted edge is given a padded delay of padding = Tj−Ti−Dmin(i, j)+Thold, the amount of delay required to satisfy the hold-time constraint. The result is GINS , a constraint graph containing optimum skews and padded delays to satisfy the optimum period, PRCA. The purpose of the last step, Parameter Minimization, is to minimize the padded delays using binary search and graph theory. For each deleted edge, the padded delay is the binary search variable in the range [0, Tj − Ti−Dmin(i, j) + Thold]. During each iteration, the same set of constraints used during CSS is used again to determine the skews Ti and Tj . There are two differences to the CSS problem: 1. The objective is to minimize the padded delays with a fixed period, whereas the objective of CSS is to minimize the period. 2. Parameter Minimization uses constraints from GINS , whereas CSS in Relax Hold uses GOCSS . Although these two graphs have different edge weights and vertex (clock skew) values, the edges from these two graphs correspond to the same physical paths in the circuit. 22 Chapter 2. Background The final output of the algorithm includes the period and skew schedule. One advantage of this approach is that it can be easily integrated with the graph theory based binary search approach in [11], which is more efficient when the skew values become quantized, and is easier to implement. 2.5 Activity Estimation To obtain accurate power estimations, a good method to calculate activity is needed. The ACE tool [20] is one such approach. This section will summarize concepts related to activity, followed by a description of the ACE algorithm. 2.5.1 Terminology There are three concepts that define the switching characteristics of a circuit. Static proba- bility (P1) is the probability that a signal is in the high (1) state. The switching probability (Ps) is the probability for a signal to change steady state value (0 to 1 or 1 to 0) at the end of a clock cycle. These transitions are a result of circuit operation, and are called functional logic transitions. Switching activity (AS) is the probability of a signal going from 0 to 1 or 1 to 0 during each clock cycle. In this thesis, switching activity will be simply referred to as activity. For a logic gate, the activity of its output is the combined result of two kinds of logic transitions: functional and glitch. Glitching results from input data signals arriving at different times during the period, causing the output to fluctuate before settling down. 2.5.2 Simulation-Based Activity Estimation ACE-2.0 [20] computes switching activities using logic simulation using pseudo-random input vectors and net delays generated by VPR. This is the most accurate method to obtain both functional and glitch activity for any arbitrary placement and routing solution 23 Chapter 2. Background 1: for all vector ∈ vector array do 2: update primary inputs(circuit, vector); 3: propagate events(circuit); 4: update flip flops(circuit); 5: end for Figure 2.12: Main ACE Algorithm produced for any FPGA architecture, and will be used as a basis in this thesis. The name “ACE” shall be used throughout this thesis to refer to this simulation-based technique. The main ACE algorithm is shown in Figure 2.12. For every input vector that represents a clock cycle, the primary inputs are loaded, the entire circuit is evaluated, and FF outputs are updated for the next vector. The main simulation routine, propagate events, is shown in Figure 2.13. It uses event-driven simulation, where each event represents a change (from 0 to 1 or 1 to 0) of an input signal to some node in the circuit. Events are queued in a list, sorted according to the event time relative to the start time of the cycle, and are examined in order by a loop (line 2). The event’s signal value is used to evaluate the node’s output value (line 4) via SIS [1], a logic synthesis tool. For example, if a 0 to 1 transition is detected, the time of the transition is compared to the time of the last transition from 1 to 0 (Time0(n), line 8). This detects the width of the most recent 1→0→1 pulse. If the pulse width is lower than a threshold (MIN PULSE WIDTH), then it is assumed that the pulse will get filtered out by a standard length-4 segment of routing track, and the toggle count for node n’s output is decremented (line 9). In the case of a long pulse, the transition will be pushed onto the event queue (line 22). 2.6 Dynamic Power Calculation Dynamic power is defined by P = α × C × V 2dd × f , where α is switching activity, C is capacitance, Vdd is supply voltage and f is operating frequency. For 65nm technology, Vdd 24 Chapter 2. Background 1: event = queue pop (queue); 2: while event != NULL do 3: n = event→fanout node; 4: value = evaluate logic(n, event→value); 5: if Value(n) != value then 6: if value == 1 then 7: //transition from 0→1 8: if event→time > MIN PULSE WIDTH && event→time − Time0(n) < MIN PULSE WIDTH then 9: Num Transitions(n) −= 2; 10: else 11: Time1(n) = event→time; 12: end if 13: else 14: //transition from 1→0 15: if event→time > MIN PULSE WIDTH && event→time − Time1(n) < MIN PULSE WIDTH then 16: Num Transitions(n) −= 2; 17: else 18: Time0(n) = event→time; 19: end if 20: end if 21: Value(n) = value; 22: push event (queue, n, event→time, value); 23: Num Transitions(n)++; 24: end if 25: end while Figure 2.13: ACE: propagate events(circuit) 25 Chapter 2. Background Figure 2.14: Architectural Modification for GlitchLess is 1V. The power figure we will refer to in this work is the power per operation, namely Pop = α× C. A power unit Pop is defined as 1 femto-Farad of capacitance switching once per clock cycle (α = 1). 2.7 Glitch Reduction GlitchLess reduces glitching by delaying early arriving signals to prevent the output from fluctuating [19]. To realize this, PDEs are added to LUT inputs according to various schemes outlined in [19], and the basic technique in shown in Figure 2.14. Other work done to reduce glitching include [12], which uses routing techniques, and [7], which proposes a new glitch-driven technology mapping tool. The first routine of the GlitchLess algorithm, calc needed delays, is shown in Fig- ure 2.15. It does a timing analysis for the circuit based on the net delays produced by 26 Chapter 2. Background VPR. The circuit is represented using a graph, with nodes representing LUTs and FFs, and edges representing the delay from node to node. For each node, a quantity called needed delay is calculated for each fanin path that represents the amount of delay that must be added to the LUT input to ensure all input signals arrive at the same time. Then, config LUT input delays function (Figure 2.16) will assign discrete delays to the LUT in- puts. To account for variation, all delays are shortened by an amount d so critical path will not be increased. 1: for all node n ∈ circuit do 2: //in topological order beginning from the primary inputs 3: Arrival Time(n) = 0.0; 4: for all fanin f ∈ n do 5: if Arrival Time(f) + Delay(n, f) > Arrival Time(n) then 6: Arrival Time(n) = Arrival Time(f) + Fanin Delay(n, f); 7: end if 8: end for 9: end for 10: for all node n ∈ circuit do 11: //in topological order beginning from the primary inputs 12: for all fanin f ∈ n do 13: Needed Delay(n, f) = Arrival Time(n) − Arrival Time(f) − Fanin Delay(n, f); 14: end for 15: end for Figure 2.15: GlitchLess: calc needed delays(circuit) (from [19]) 27 Chapter 2. Background 1: for all LUT n ∈ circuit do 2: count = 0; 3: for all fanin f ∈ n do 4: if Needed Delay(n, f)>min in && Needed Delay(n, f)≤max in && count<num in then 5: Needed Delay(n, f) −= min in * floor(Needed Delay(n, f)/min in); 6: count++; 7: end if 8: end for 9: end for Figure 2.16: GlitchLess: config LUT delays(circuit, min in, max in, num in) (from [19]) 28 Chapter 3 Glitch Generation Modelling This chapter presents a modified approach for glitch power estimation via vector simulation- based, event-driven activity calculation. The ACE framework is used as a basis. The purpose of the modification is to obtain a more accurate model for power estimation due to glitch generation. Throughout this thesis, all benchmarks are simulated with 5000 pseudo-random input vectors (clock cycles). 3.1 Introduction and Motivation The ACE tool filters out fluctuations of very short pulse widths since the routing resource’s parasitic capacitance can dampen them out. Originally, the maximum pulse width that can be filtered out by a single stage of length-4 routing segment was determined by HSPICE simulation. A glitch longer than this threshold is assumed to go on indefinitely, otherwise it is assumed to consume no power. Neither of these assumptions is true in reality: as long as the pulse width of a glitch is below a certain threshold (short glitch), it will be gradually filtered out after propagating down a certain number of wire segments. Glitches longer than the threshold may propagate indefinitely. To take this into account, we first simulate routing nets of various lengths in Cadence Spectre (an accurate alternative to HSPICE) to obtain glitch behavior, then modify ACE to determine a pulse width histogram, and finally combine the results with VPR to calculate power. 29 Chapter 3. Glitch Generation Modelling Figure 3.1: Length-4 Wire Stage Overall View Figure 3.2: Fragment Detail View 3.2 Cadence Simulation Cadence Spectre simulations are done for glitches of varying pulse widths travelling down various lengths of routing nets. A standard length-4 segment, called a stage, is modelled as in Figure 3.1, which contains driving buffers and 4 individual fragments of wire each spanning the length of one CLB. This CLB length is 125µm throughout the thesis. A pair of multiplexers are connected to the junction between fragments to indicate possible branch-off points. Each fragment is shown in Figure 3.2, where each multiplexer at the branch-off point is simulated as a minimum sized NMOS in cutoff mode. For accuracy, each fragment is modeled as a 4-piece pi model [34]. A short glitch of a particular pulse width, travelling down a routing track will have its pulse width being gradually decreased by the routing resource’s parasitic RC effect. Consider a 0-1-0 pulse travelling down a single stage: The rising edge needs a certain time for the output to rise to the supply voltage level because the capacitance need to charge 30 Chapter 3. Glitch Generation Modelling 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 1800 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Glitch Pulse Width (ps) N or m al iz ed P ow er 10 stage 9 stage 8 stage 7 stage 6 stage 5 stage 4 stage 3 stage 2 stage 1 stage Figure 3.3: Segment Length to Power Lookup (65nm Technology) up. If the pulse width is too short, the output may not have enough time to reach the supply voltage before the falling edge starts, which effectively reduces the peak voltage of the pulse created at the output. As a short glitch travels down a routing track of n stages, the decreasing pulse width causes power consumption to decrease because the routing capacitance does not fully charge. The power consumed by a short glitch can be expressed as a percentage normalized to that consumed by a long glitch propagating down the same n stages. Simulation results for 1 to 10 stages are summarized in Figure 3.3. A converging trend is observed as the lines get closer together for increasing number of stages. Therefore, it is assumed that any net longer than 10 stages (wire segments) will behave the same as a 10-stage net. To pass this information to VPR, a lookup table of percentages are created from Figure 3.3, whose x-axis is divided into bins. Each bin is 5ps wide. For each stage length, the power percentage for each bin is calculated as an average of its lower and upper boundary values. For example, in Figure 3.3, a 42ps pulse travelling down 1 stage will have a percentage of 31 Chapter 3. Glitch Generation Modelling (0.3+0.35)/2=32%. Any short glitch longer than 180ps consumes nearly the same power as a long glitch. Therefore, 180ps is used as the upper threshold to determine if a glitch can propagate indefinitely or not. Furthermore, any glitch shorter than 15ps consumes nearly zero power, so 15ps is used as the lower threshold to determine if a glitch should be ignored. 3.3 Glitch Binning Algorithm Description The majority of the ACE program is unchanged from the last chapter, with the exception of the propagate events routine. This section will detail the changes. 3.3.1 ACE Inputs The program requires the following input files: 1. A BLIF file of the circuit to build circuit diagrams for timing analysis and logic evaluation. 2. A vector file containing pseudo-random input vectors for circuit primary inputs. 3. A net delay file produced by VPR, containing net delays for all nodes in the circuit, after routing is finished. 3.3.2 Event Propagation Modifications to ACE include changes to the propagate events routine to calculate glitch pulse widths, and to group glitches of different pulse widths into bins (for example, glitches ranging from 15ps to 20ps is bin #1, etc). The algorithm is outlined in Figure 3.5. Note the distinction between a transition and a pulse: a pulse (1-0-1 or 0-1-0) consists of two back-to-back transitions. 32 Chapter 3. Glitch Generation Modelling Figure 3.4: Glitch Filtering Effect When a 0 to 1 transition is detected (line 6), and the width of the 1-0-1 pulse is below threshold (line 7), several things can happen. If the signal at the beginning of the cycle is 1, then this transition is the finishing edge of a pulse. Since the pulse width is too small, no action needs to be taken. If the signal at the beginning of the cycle is 0, then a 0-1-0 pulse precedes this transition. We need to “merge” it into the previous pulse as if this pulse has never happened, as shown in Figure 3.4. This is done by decrementing the glitch count (line 10 and 11). If the 1-0-1 pulse width is above threshold, it is a glitch and should be accounted for, but only if the signal started with a value of 1 at the beginning of the cycle, because only then a 0-1 transition is the completing transition of a pulse. In other words, if the starting value is 0, then a 0-1 transition is always the starting edge of a pulse, so the glitch count should not be incremented yet. Lines 15 to 20 calculates the pulse width and increments the corresponding bin number. The case for a 0-1-0 pulse (starting from line 23) is similar. 3.3.3 ACE Output ACE provides a single output text file containing the activity breakdown of all nodes in the circuit, with names taken from the BLIF file. For each node, P1, PS , AS and total glitch activity summed over all nodes are printed, as well as the number of glitches in each bin. 33 Chapter 3. Glitch Generation Modelling 1: event = queue pop (queue); 2: while event != NULL do 3: n = event→fanout node; 4: value = evaluate logic(n, event→value); 5: if Value(n) != value then 6: if value == 1 then 7: if event→time > MIN PULSE WIDTH && event→time − Time0(n) < MIN PULSE WIDTH then 8: Num Transitions(n) −= 2; 9: if Prev Value(n) == 0 then 10: Num Glitch(n)−−; 11: Num Glitch Bin(n, Prev Glitch Bin(n))−−; 12: end if 13: else 14: Time1(n) = event→time; 15: if Prev Value(n) == 1 then 16: Num Glitch(n)++; 17: pulse width = event→time - Time0(n); 18: bin = (int)((pulse width − MIN PULSE WIDTH)/BIN WIDTH); 19: Num Glitch Bin(n, bin)++; 20: Prev Glitch Bin(n) = bin; 21: end if 22: end if 23: else 24: if event→time > MIN PULSE WIDTH && event→time − Time1(n) < MIN PULSE WIDTH then 25: Num Transitions(n) −= 2; 26: if Prev Value(n) == 1 then 27: Num Glitch(n)−−; 28: Num Glitch Bin(n, Prev Glitch Bin(n))−−; 29: end if 30: else 31: Time0(n) = event→time; 32: if Prev Value(n) == 0 then 33: Num Glitch(n)++; 34: pulse width = event→time - Time1(n); 35: bin = (int)((pulse width − MIN PULSE WIDTH)/BIN WIDTH); 36: Num Glitch Bin(n, bin)++; 37: Prev Glitch Bin(n) = bin; 38: end if 39: end if 40: end if 41: Value(n) = value; 42: push event (queue, n, event→time, value); 43: Num Transitions(n)++; 44: end if 45: end while Figure 3.5: Modified ACE Routine: propagate events(circuit) 34 Chapter 3. Glitch Generation Modelling 3.4 Power Calculation To calculate total dynamic power consumption, ACE output and Spectre simulation results are read into VPR as separate input files (detailed discussion about VPR modifications are presented in Chapter 4). For a glitch generated at the source node of a net in the circuit, the length and capacitance of the routing track for that net is determined with the VPR routing graph, the glitch activity for each bin is read from ACE, and the amount of glitching power can be calculated by multiplication of capacitance, glitch activity, and the percentage found in Fig 3.3 via indexing by net length and bin #. There are other components that consume dynamic power, namely intra-CLB routing and MOSFETs that make up LUTs and SEs. The former is dominant because a feed- back wire from LUT output to LUT input MUXes in the same CLB carries much more capacitance than the latter, which we neglect in our calculations. 3.5 Results and Discussion The results from the original ACE, and those obtained from the new glitch binning algo- rithm, are compared in Table 3.1 for circuits produced by VPR. Units are Pop described in Chapter 2. All circuits are simulated using 5000 pseudo-random input vectors. A positive percentage difference means the original ACE underestimates glitching. The original ACE can underestimate glitching power as much as 48%, for k=4, and overestimate as much as 15% for k=6. Generally, original ACE underestimates glitch power for k=4 because arrival time differences for a smaller LUT tend to be smaller and get dropped (below threshold). Our glitch power estimation can still be improved further. Glitch filtering creates two issues: glitch generation and propagation. The former is a glitch created at the output of a gate generated by the combined effect of its logic function and different input arrival times. 35 Chapter 3. Glitch Generation Modelling circuit k = 4 k = 6 Bins Original % diff Bins Original % diff bigkey 913 471 48.4 560 629 -12.4 clma 3794 3407 10.2 2955 3303 -11.8 diffeq 136 129 4.9 63 58 6.7 dsip 698 512 26.6 574 557 3.2 elliptic 11607 10462 9.9 6408 6944 -8.3 frisc 1185 1096 7.5 1045 1088 -4.1 s298 5350 3906 27.0 4956 5585 -12.7 s38417 29292 19195 34.5 7036 8111 -15.3 s38584.1 10455 9246 11.6 4052 4395 -8.5 tseng 1334 1326 0.6 590 608 -3.2 Table 3.1: Glitch Power (Pop) of Original ACE and ACE with Binning The latter is illustrated by the example in Figure 3.6. A glitch generated at the source node, BLEA in CLB1, fans out to two sink nodes: BLEB in CLB2 and BLEC in CLB3. Short glitches become narrower as it travels along a routing segment, therefore the glitch will become narrower at the input of the fan out nodes. Also, the glitch travels a longer distance to BLEC than it has to BLEB, so the pulse width of the glitch at the input of BLEC is narrower than that at the input of BLEB. Therefore, the glitch pulse width at the output of BLEC is narrower than that of BLEB, and may become smaller than the lower threshold defined earlier. Proper glitch propagation modelling takes all of these into consideration. Our work has a better estimate on glitch generation and power consumed by the rout- ing that immediately follows a glitchy node, but it still lacks proper glitch propagation modelling. In particular, the new glitch binning algorithm does not shorten the width of the pulses as they are propagating through logic during the event-driven simulation. For a complete analysis, we need to account for the change of glitching activity on downstream nodes caused by glitch propagation, requiring VPR and ACE to be tightly integrated so 36 Chapter 3. Glitch Generation Modelling Figure 3.6: Glitch Propagation logic evaluation and routing information can be obtained concurrently. 37 Chapter 4 Architecture and Algorithm Overview A major contribution of this work is the proposal of a unified architecture change that can be shared by CSS, delay padding and GlitchLess, and the integrated tool flow. This section will detail this architecture as well as its adaptation by each of the 3 optimization steps. We assume that newer FPGAs, such as the Stratix III and Virtex 6, have 2 flip flops per LUT. A discussion of the tool flow, PGR algorithm and preliminary operations follow. 4.1 Architecture 4.1.1 Architecture for CSS and Delay Padding Architecture changes are highlighted in Figure 4.1 with legends shown to distinguish opti- mization steps. CSS can be done by adding delay δA to FFA. For delay padding, we use local rerouting within CLBs. The CLB input (solid arrow line) in Figure 4.1 goes to LUTB originally. We reroute it (dash-dotted line) to unused FFC in another BLE, then back to the original LUT. Properly adjusting the skew assigned to δC , any desired delay can be achieved provided there is enough slack for it. 38 Chapter 4. Architecture and Algorithm Overview Figure 4.1: Unified Architecture Modification 39 Chapter 4. Architecture and Algorithm Overview 4.1.2 Architecture for Glitch Reduction To eliminate glitching on a combinational node, we use a circuit level architecture change different from that analyzed in [19]. Instead of inserting a PDE at LUT inputs, we achieve glitch reduction by directing the LUT output to FFD, whose clock skew δD will be set to the latest arrival time of all LUT inputs plus a setup time and timing margin. The LUT output fluctuates, but the FF will block all glitches until the final functional evaluation is known. Our approach requires only one PDE to eliminate the glitching for each LUT, compared to k-1 PDEs for each LUT used in [19]. One disadvantage of this approach is the fact that clock has an activity of 1. Compared to PDEs inserted into the data lines with relatively low activity (say, between 0.05 and 0.2), this approach may introduce a significant power overhead. We will show how this affects the results in Chapter 6. 4.1.3 Alternative PDE-Sharing Architecture In an effort to reduce area and power overhead, an alternative architecture is proposed where each FF in the CLB can select one of several PDEs shared by the PDE as shown in Figure 4.2. The number of PDEs available for sharing is an user-determined architecture parameter. 4.1.4 Interaction between CSS and GlitchLess CSS and GlitchLess share the same architecture change, this is beneficial because applying CSS usually also increases glitching. In this section, we discuss how CSS affects glitching, and how can GlitchLess make use of the CSS architecture to reduce glitching. Glitching can account for a large portion of dynamic power. Although it varies with every circuit, some correlation can be drawn between the amount of glitching and several circuit characteristics. 40 Chapter 4. Architecture and Algorithm Overview Figure 4.2: PDE Sharing Architecture 41 Chapter 4. Architecture and Algorithm Overview circuit k = 4 k = 6 de pt h % F F g li tc h p r e C S S d y n a m ic P o w er g li tc h p o s tC S S d y n a m ic P o w er de pt h % F F g li tc h p r e C S S d y n a m ic P o w er g li tc h p o s tC S S d y n a m ic P o w er bigkey 5 13.1 1.9 4.2 5 32.4 1.6 2.3 clma 18 0.4 6.2 11.5 12 0.5 6.2 7.3 diffeq 16 25.2 3.4 2.7 10 43.4 2.1 1.6 dsip 5 16.4 1.3 1.7 5 32.6 1.4 1.8 elliptic 20 31.1 21.0 24.0 12 52.6 15.9 21.9 frisc 25 24.9 13.0 16.8 16 30.1 13.4 15.9 s298 17 0.4 21.7 29.5 13 0.6 23.2 28.2 s38417 13 22.8 26.8 28.3 9 43.2 14.4 24.4 s38584.1 11 19.7 7.8 9.4 9 29.4 4.0 5.57 tseng 15 36.8 15.3 15.1 10 48.2 8.4 11.9 average 11.8 14.3 9.1 12.1 Table 4.1: Sequential Circuit Characteristics and Glitching Power In Table 4.1, we compare circuit depth and sequential element density of the ten biggest sequential MCNC circuits mapped to 4-LUTs and 6-LUTs to the percent of dynamic power that is due to glitching. The data shows when a circuit has high depth, and low percentage of nodes being sequential, the glitching is high. This is intuitive because FFs block glitching, and creates “flatter” circuits with less depth and less possibility for glitches to travel far. While glitching is insignificant for some circuits, there is motivation for glitch reduction for other circuits where the glitch power accounts for up to 30% of the dynamic power. Also, a 6-LUT architecture tends to have less glitching because fewer LUTs are needed to implement logic. This decreases inter-CLB routing, which has most of the capacitance. CSS perturbs glitching. All FFs have the same signal departure time in zero-skew circuits, but skew assigned to SEs effectively delay that time, changing the amount of glitching created downstream. In Table 4.1, the preCSS and postCSS columns show the amount of dynamic power due to glitching before and after CSS with delay padding has 42 Chapter 4. Architecture and Algorithm Overview been performed, respectively. In most circuits, the amount of glitching increases by a fair margin after CSS. This further motivates the need for glitch reduction. 4.2 Algorithm Overview The overall approach is illustrated in Figure 4.3. It offers three optimization choices. The first choice (choice “1”) uses the original VPR placement and routing solution to generate a net delay file for ACE, which produces an activity file for PGR to do glitch reduction only. The resulting net delays are analyzed by ACE again to produce final activities, and the power analysis routine of PGR is used to determine power savings. Alternatively, the place and route solution can be used directly by PGR to do CSS and delay padding, followed by ACE simulation (choice “2”), and power estimation of the clock scheduled circuit. The user may choose to use the activity file from the CSS solution to do further glitch reduction (choice “3”), followed by ACE to get power results. Note that ACE needs to be run twice in choice 1 and 3, because node activities are dependent on the net delays, which changes after either CSS or GlitchLess. In CSS, different arrival times caused by skews to the FFs changes the downstream arrival time for combinational nodes, while GlitchLess delays the arrival time of combinational nodes in consideration of safety margins. PGR is integrated with VPR into a single executable, and is invoked after VPR place- ment and routing. The required input files are summarized below. 1. Netlist, architecture, placement and routing files required for VPR. 2. BLIF file required for PGR’s data structure building. 3. Activity file from ACE, for power estimation and GlitchLess. 43 Chapter 4. Architecture and Algorithm Overview Figure 4.3: Top Level Algorithm 4. Power lookup file from Cadence simulations described in the previous chapter, for power calculations. The output of the program is a delay file containing net delays between all nodes in the circuit. Depending on user choice, the delay file can be produced immediately after VPR finished, or after GlitchLess, CSS, or both. In addition, if CSS is performed, the program will output the final skew schedule produced as well as the corresponding period. 4.3 PGR Overview The top-level PGR algorithm, shown in Figure 4.4, builds the necessary data structures for CSS and GlitchLess, and controls the main operation. This section explains some preliminary actions that build the circuit data structure.. 4.3.1 Node Building Stage The first step in the node building stage groups all nodes in the circuit into an array, such that every node is placed after all of its transitive fanins, which include all nodes in its fanin cone. The net delay information is obtained from VPR, and the BLIF input file is used to identify FFs. To avoid cycles, each FF is broken up into two nodes: A virtual 44 Chapter 4. Architecture and Algorithm Overview Figure 4.4: PGR Top Level Algorithm 45 Chapter 4. Architecture and Algorithm Overview output (has no fanout nodes) representing the D input, and a virtual input (has no fanin nodes) representing the Q output. This ordering of nodes is convenient for timing analysis, which is an important aspect of the program. For GlitchLess, a breadth-first ordering of the nodes is created for topological access. For CSS, a separate data structure is created that represents every FF as one node each to detect negative cycles when the Bellman-Ford algorithm is performed. Special attention is paid to BLEs in sequential mode. One example is shown in Figure 4.5. In the BLIF file, three signals (named int2, int3 and int4) converge at a OR gate, whose output (named int5) goes directly to a FF. After placement and routing, the OR gate and the FF are absorbed into a single BLE operating in sequential mode, and the signal int5 disappears in the VPR netlist. The original circuit information needs to be preserved for correct interfacing with ACE, which does not have access to the VPR netlist. To accomplish this, each FF net is read from the BLIF file and compared to the VPR netlist to detect this situation. Three nodes are created in PGR, one for the OR gate, and two for the virtual source/sink pair. In addition to routing, intra-CLB connections also add to net delays. However, the timing graph produced by routing only includes delays of inter-CLB routing. Therefore, these timing parameters are copied out of the architecture file and added in the PGR code. 4.3.2 Node Attribute Calculation The next stage of preliminary operations is to assign each node its output capacitance, activity and power. Capacitance information is mainly obtained from the VPR routing graph for each net that fans out to inter-CLB routing. For muti-fanout nets, the algorithm calculates the length, in terms of length-4 segments, for each source to sink connection in the net. Intra-CLB capacitance is not included in VPR, and has to be calculated and 46 Chapter 4. Architecture and Algorithm Overview Figure 4.5: BLE in Sequential Mode Figure 4.6: Calculating Intra-CLB Capacitance 47 Chapter 4. Architecture and Algorithm Overview added to the inter-CLB capacitance. Previous work [26] has a detailed model to compute intra-CLB capacitances. This thesis uses a simplified version of those calculations, and we believe it captures the most important parts of intra-CLB capacitance. Figure 4.6 illustrates how this is done. Each CLB input (I1, I2) connects with a 1X buffer followed by 4X buffer, with total capacitance Cbuffer. The input then connects to a wire that runs the entire length of the CLB. This wire is named ClocalWire, and is local type routing that has a capacitance less than the general routing resource in between CLBs. This capacitance is separately calculated using PTM models [3]. Each BLE output is connected to a wire with approximately twice the capacitance of ClocalWire because it needs to be routed back to the input MUXes in an L shape. Each CLB input and BLE feedback connects to half of the input MUXes, according to a 50% connectivity stated in [23]. Therefore the capacitance, named CintraCLBnear, is calculated as follows: CintraCLBnear = Cbuffer + 2 · ClocalWire + numMUX · C1XNMOSdrain numMUX = inputsPerLUT · numLUTsPerCLB · CONNECTIV ITY = 0.5 · k ·N In addition to the above equation, a BLE may fan out to inputs of other CLBs in the circuit via inter-CLB routing. A glitch generated by the BLE will propagate to these fan out CLBs, the buffers immediately after the CLB inputs, and the BLE input connections in the fan out CLBs. This capacitance is named CintraCLBfar and is calculated as follows: CintraCLBfar = Cbuffer + ClocalWire + numMUX · C1XNMOSdrain After activities are read in from the activity input file from ACE, and power percentages are read from the power lookup file, the power consumed by each net is calculated with the following equation: 48 Chapter 4. Architecture and Algorithm Overview Power = CintraCLBnear · ∑ ibins Glitchi + ∑ ibins ( ∑ jfanouts (CintraCLBfar + Crouting) ·Glitchi · powerLookup[lengthj ][i]) where Crouting is the capacitance of the routing trace between the BLE and the fanout CLB, Glitchi is the number of glitches that belong to i, the ith bin, j is each fanout that connects to another CLB, and lengthj is the length of the routing track, in terms of length-4 segments, that connects to that CLB. 49 Chapter 5 Detailed Algorithm Description This chapter discusses the two main routines of the PGR algorithm: CSS and GlitchLess. The interaction between these two modules and the overall algorithm is explained in detail. 5.1 CSS and DP Algorithm The CSS algorithm uses a binary search technique combined with the Bellman-Ford algo- rithm to iteratively find the minimum period, then uses delay padding to further improve results. The procedure is repeated twice if there is a restriction on the number of PDEs available to each CLB, in an effort to reduce the period penalty while using as few PDEs as possible. 5.1.1 Motivation CSS and DP have been explored in previous research, and a checklist of features they offer is compared against that of this thesis in Table 5.1. In the CSS-only area, both ASICs and FPGAs have been considered with variation modelling and solved using efficient graph algorithms. However, the work for FPGAs [28, 31, 36] does not consider delay padding. The work in [18, 33] considers both CSS and DP but is targeted to ASICs only. Variation modelling is included in [33], but it uses the less efficient LP method. In contrast, [18] uses more efficient graph algorithms, but lacks variation modelling. In addition, both of these 50 Chapter 5. Detailed Algorithm Description Brown [28] Feature Sapatnekar [11] Sadowska [36] Kourtev [33] Lu [18] This Bazargan [31] Thesis Platform ASIC FPGA ASIC ASIC FPGA CSS Delays continuous discrete continuous continuous discrete Variation yes yes yes no yes Platform ASIC ASIC FPGA DP Delays continuous continuous discrete Variation yes no yes Algorithm graph graph LP graph graph Table 5.1: Feature Comparison approaches use a continuous delay model, which is not realizable with PDEs. Our post-CSS delay padding algorithm offers the following novelties. It targets FP- GAs, and it is aware of discrete delay steps, process variation margins that limit both the minimum and maximum delay that can be assigned to a node, and the possibility that delay padding may fail due to these margins. To the best of our knowledge, this is the first time delay padding has been applied to FPGAs. 5.1.2 Algorithm Description The top-level CSS algorithm is shown in Figure 5.1 and described in more detail in Figure 5.2. The “CSS” box in Figure 5.1 refers to lines 6 to 14 in Figure 5.2, and the “DP” box refers to lines 15 to 21. The first step uses the result of the node building stage (Section 4.3.1) to produce a circuit that contains CSS nodes to represent each FF, and edges representing difference constraints (Chapter 2.2.1). This is done in the build CSS nodes() function. Maximum and minimum path delays between each pair of CSS nodes is calculated using the method in [14]: the current source’s arrival time is set to zero while all others’ are set to infinity. After latest arrival time propagation, all sinks reachable from the source will have non-infinity 51 Chapter 5. Detailed Algorithm Description Figure 5.1: CSS and Delay Padding Flow Chart 52 Chapter 5. Detailed Algorithm Description 1: //start two-pass procedure 2: for pass=0 to 1 do 3: iteration = 0; 4: build CSS nodes(); 5: analyze path delays(); 6: solution[iteration] = assign skews(Pmax, Pmin); 7: num edges = find crit hold edges(edges[iteration]); 8: while num edges > 0 do 9: find deleted edge nodes(); 10: recalculate PmaxandPmin using recalc binary bounds(); 11: iteration++; 12: solution[iteration] = assign skews(Pmax, Pmin); 13: num edges = find crit hold edges(edges[iteration]); 14: end while 15: while iteration > 0 do 16: success = pad delay(edges[iteration],solution[iteration]); 17: if success then 18: break; 19: end if 20: iteration−−; 21: end while 22: if pass==0 then 23: if solution is achievable with number of PDEs available for sharing for all CLBs then 24: break; 25: end if 26: end if 27: end for Figure 5.2: CSS and Delay Padding Algorithm 53 Chapter 5. Detailed Algorithm Description arrival times representing Dmax. A similar process is repeated to find Dmin using negative infinity and earliest arrival time propagation. This is done in the analyze path delays() function. The binary search method with Bellman-Ford algorithm in Figure 2.3 [11] is then applied inside the assign skews() function. During each successful iteration in the binary search, where there are no negative cycles in the solution, each negative skew assigned by Bellman-Ford is adjusted to a positive value by adding the magnitude of the most-negative skew to the entire set of skews. Then, all skews are quantized, a procedure that snaps each skew to its nearest time unit that can be realized using a programmable delay element. This will cause a slight perturbation to the optimized period, and is calculated accordingly. The final period and skew schedule is stored in a solution array (line 6 in Figure 5.2). After the initial CSS run, all critical hold-time edges found with the current skew schedule are removed and appended into a list of currently deleted edges [18] using find crit hold edges(). The combinational LUTs on each critical edge are identified and put into arrays with find deleted edge nodes(). The assign skews() routine is run again with recalculated binary search bounds ignoring the deleted edges. The Bellman-Ford al- gorithm will ignore the hold-time constraint of the deleted edges, so their violation will not cause the negative weight cycle flag to go positive. This iterative process is repeated until no more critical edges can be found, and the end result contains the skews, period and deleted edges found with each iteration, all stored in arrays. In the next step, the algorithm will attempt to pad delays for all deleted critical edges from the most current iteration. In case delay padding is not successful, additional attempts will be made for earlier iterations until a valid padding solution is achieved. The detailed delay padding algorithm is shown in Figure 5.3. For each deleted edge, the algorithm traces the combinational path from the source to sink in a topological breadth-first order. When 54 Chapter 5. Detailed Algorithm Description each node is visited, it tries to delay the signal as late as possible without violating setup constraints. In other words, the padded delay must be smaller than the available slack, which is calculated on line 6 of Figure 5.3, and is equal to the required time of the node “n” we are trying to delay (n→required) minus the latest signal arrival time of the fanin node (fanin→arrival) and the path delay between the fanin node and n (fanin delay(n, fanin)). To add delay to node n, its skew is first set to the arrival time of the fanin node plus the setup time and variation margin, rounded up to the nearest discrete time unit specified by the parameter “PRECISION”. The variation margin added here is to compensate for an early arriving clock, which could cause the data signal to be late. In the case the clock arrives late, the data path has to have enough slack to compensate for it (line 9). This check is done with line 10: a node is skipped in the delay padding process if there is not enough slack to compensate for both late and early arriving clocks. To complete delay padding for the current edge, the quantity “delay” is incremented in quantized steps (lines 13 to 15), until either the node’s available slack runs out, or the needed delay is satisfied. If padding one node is not enough to satisfy the hold-time constraint for the entire path, other nodes on the path are then considered for delay padding as well. When delay padding for an edge finishes, check other paths() is used to check for other short delay paths (with the same source and sink) violated. Any such violations found are fixed in the same way, until setup and hold-time constraints for all combinational paths with the same source and sink are satisfied. In this thesis, “PRECISION” and “MARGIN” in Figure 5.3 are chosen to be 0.1ns, or 1 discrete step, so that each PDE has at 0.1ns of uncorrelated variation tolerance between its assigned skew and path delay, for early and late clock signals. The margin M for CSS is 0.2ns, allowing Ti and Tj (Eqs. 2.2,2.3) to each shift 0.1ns away from each other, for example. In reality, timing margins should be a percentage of the magnitude of the delay, 55 Chapter 5. Detailed Algorithm Description 1: success = 1; 2: for all edge ”iedge” in deleted edges list do 3: needed delay = calculate needed delay(iedge); 4: for all node ”n” on deleted edge ”iedge” do 5: analyze timing(); 6: available slack = n→required - fanin→arrival - fanin delay(n, fanin); 7: skew = roundup(fanin→arrival + Ts + MARGIN + fanin delay(n, fanin), PRE- CISION); //for early arriving clock 8: delay = skew − fanin→arrival − fanin delay(n, fanin); 9: needed slack = delay + MARGIN; //for late arriving clock 10: if needed slack>available slack then 11: continue; 12: end if 13: while (delay<needed delay && needed slack<max padding) do 14: increment skew, delay and needed slack by PRECISION 15: end while 16: needed delay −= delay; 17: if needed delay ≤ 0 then 18: edge done = 1; break; 19: end if 20: end for 21: if edge done == 1 then 22: check other paths(); 23: else 24: success = 0; 25: end if 26: end for 27: if !success then 28: roll back delays(); 29: end if 30: return success; Figure 5.3: Detailed Delay Padding Algorithm, pad delay() 56 Chapter 5. Detailed Algorithm Description so that longer paths have more room for error, while smaller paths can have smaller margins and take up less slack. The margins used here may not be sufficient for larger delays, and further analysis with variable timing margins will be included in future work. 5.1.3 Variable PDE-per-CLB Restriction When there is a restriction on the number of PDEs each CLB can have, a two-pass CSS approach is used to reduce the performance degradation due to decreased PDE availability, and the power consumption overhead. The number of PDEs available to each CLB is an user specified command line parameter. In the first CSS pass, the algorithm in Figure 5.2 is performed assuming there is no PDE restriction. At the end of the assign skews() and try pad delay() routines, the algorithm will determine the number of distinct skews needed in each CLB for CSS and DP. For example, if a CLB has six FFs with the skew set {1.2, 1.2, 1.6, 1.6, 1.6, 1.8}, and three combinational LUTs delay padded with the skews {1.2, 2.3, 3.0}, then this CLB needs five distinct skews {1.2, 1.6, 1.8, 2.3, 3.0}, three of which are needed for CSS ({1.2, 1.6, 1.8}) and three for DP ({1.2, 2.3, 3.0}). At the end of the first pass, the necessity of the second pass is determined by comparing the required number of PDEs in each CLB to the number of PDEs available. If there exist any CLBs with more skews needed than the number of PDEs available, a second pass is needed. In the above example, if there are less than five PDEs to share, then the second pass is necessary. In the second pass, the build CSS nodes() function at line 4 in Figure 5.2 differs from the first pass, where each FF is treated as a distinct node in the CSS graph. The situation for each CLB in the second pass can be one of two cases, illustrated using the above example. 57 Chapter 5. Detailed Algorithm Description In the first case, there are enough PDEs to satisfy the number of skews needed by CSS alone. This number is 3 in the example, for skew values of 1.2, 1.6 and 1.8. There are 2 FFs with skew 1.2. They will be “fused” together to form a single node in the CSS graph for the second pass. The same applies to the 3 FFs with skew 1.8. In the end, the 3 PDEs become 3 CSS nodes, and each may contain more than one FF. In the second case, there are not enough PDEs to satisfy all needed skews. In the ex- ample, if there are fewer than 3 PDEs to share, we need a sharing schedule that distributes the six FFs to the 3 PDEs, such that the impact on the period is minimum. An easy way to do this is a random assignment, which has been implemented for this thesis. This may not be the optimum sharing schedule having the minimum impact on period. However, if there are only a few CLBs that need the treatment in the second case, the circuit graph used for CSS in the second pass will not be too different from that in the first pass. After CSS is done with the fused nodes, delay padding is done as usual. After the skew to be assigned to a node “n” is decided (line 15 in Figure 5.3), a check is performed to see if there is a pre-existing PDE that offers the same skew. If there are no existing PDEs that can provide the required skew, a free PDE will be used. If there are no more free PDEs available, node “n” will be skipped and the algorithm will try to pad delays for the next node. The last step of the algorithm assigns the final skew schedule to each FF in the circuit, and updates PDE usage information for each CLB. 5.1.4 Interface with GlitchLess After CSS and DP are done, skews from the graph structure used to do CSS must be added to the array of nodes built earlier (Section 4.3.1). Since each FF is split into a virtual source and a virtual sink, the skew is added to the virtual source to signify the change of signal 58 Chapter 5. Detailed Algorithm Description Figure 5.4: Adding Skew to Circuit Timing departure times, as shown in Figure 5.4. After the skews have been added, static timing analysis changes. In the example in Figure 5.4, the skew of 5 time units is added to the local path between FF2 and FF3. this will lengthen the arrival time of vsink3 to 8 instead of the 3 units of combinational delay. In a zero-skew circuit, the required time at vsink3 is the period. Since the skew of 2 at FF3 delays the departure time, the signal has more time to arrive, so the required time is period plus 2. If the period is 7, then the slack at vsink3 is required time - arrival time = 7+2-8 = 1. 5.2 Glitch Reduction Algorithm Regardless of whether CSS and DP has been done before, the GlitchLess algorithm uses static timing analysis to determine the feasibility of adding PDEs in combinational paths. 59 Chapter 5. Detailed Algorithm Description 1: for all levels in timing graph do 2: rank nodes(&list, threshold); 3: for all node ”n” in list do 4: skew = roundup(n→arrival + Ts + MARGIN, PRECISION); //for early arriving clock 5: needed slack = skew − n→arrival + MARGIN; //for late arriving clock 6: if needed slack < n→slack && PDE is available then 7: for all fanin ”f” of node ”n” do 8: needed delay = n→arrival − f→arrival − fanin delay(n, f); 9: fanin delay(n, f) += needed delay + needed slack; 10: end for 11: analyze timing(); 12: update pde usage(); 13: end if 14: end for 15: end for Figure 5.5: Glitch Reduction Algorithm 5.2.1 Motivation One significant difference of this work compared to [19] is added consideration of process variation. In [19], all added delays are shortened by an amount d so that variation will not increase the critical path. This may result in increased glitching power in practise, since narrow pulses are not “zero power” as assumed previously. The approach in this thesis eliminates this issue by stopping all glitching at the FF input, and only clocks through the data when the last signal has arrived. 5.2.2 Algorithm Description The algorithm is shown in Figure 5.5. The circuit is traversed in a breadth-first fashion so that the extra delays assigned upstream can be included and assumed to be fixed when calculating delay requirements for downstream nodes. A user-defined threshold parameter is specified to filter out nodes with small glitch activity. In each level (line 1), all com- 60 Chapter 5. Detailed Algorithm Description binational nodes are ranked according to their glitching power, so that nodes with high loading get priority during PDE delay assignment. For each node “n” that is eligible for GlitchLess, the skew is set to the node’s latest signal arrival time (n→arrival), plus the setup time and timing margins (line 4 in Figure 5.5). Similar to delay padding, a check is done to make sure node “n” has enough slack in case the clock signal arrives late (lines 5 and 6). When there is a restriction on the number of PDEs per CLB, the PDE checker (line 6, Figure 5.5) first checks to see if the needed skew can be provided by any of the CSS or DP skews, if there is no existing PDE to satisfy the skew, it then checks if a free PDE is available. After GlitchLess is applied to each combinational node “n”, static timing analysis is done on the circuit to recalculate arrival/required times, and the available number of PDEs for the CLB where “n” resides in is decremented if a free PDE is used up. (line 11 and 12, Figure 5.5). 61 Chapter 6 Experimental Results and Discussion Of the largest 20 MCNC circuits, the 10 sequential circuits are used as benchmarks, and they are simulated for k = 4 and 6, N = 10, and I = 22 and 33, respectively. A technology of 65nm is used. All results are normalized with respect to the original solution found using VPR 5.0. Architecture files are from the iFAR repository [2], and routing resource capacitance and resistance values are calculated from the PTM website [3] assuming CLBs are 125µm x 125µm in size. All circuits are simulated using timing-driven placement and routing with a channel width of 104, which is sufficient to route all of these circuits. Area estimation is done with a channel width of 200, which represent the capacity of today’s low-cost FPGAs. Activity estimation is produced with the modified ACE described in Chapter 3, simulated with 5000 pseudo-random input vectors. 6.1 CSS-Only Results 6.1.1 Performance In Table 6.1 and 6.2, we demonstrate the period reduction (as a percentage of original critical path) we are able to obtain from CSS and delay padding, for k=4 and 6, respectively. The design flow follows choice “2” in Figure 4.3. The “20 PDE” column represents the 62 Chapter 6. Experimental Results and Discussion architecture where we allow each FF to have its own PDE, and the other columns represent the case where we fix the number of PDEs that are shared by FFs in each CLB. The CSS column represent CSS-only results, and the CSS+DP column represent CSS+DP results. When the result for CSS and CSS+DP are the same, the 2 columns are merged into one cell. The benchmarking for k=4 stops at 6 PDEs per CLB, and at 7 PDEs per CLB for k=6 because further increasing the number of PDEs per CLB does not incur any additional performance benefit. When each FF has its own PDE, average speedups of 13% (k=4) and 17% (k=6) are obtained for CSS alone. Delay padding further improves 4 circuits for both k values. For elliptic, frisc and tseng, delay padding is very helpful. CSS alone reduces period by up to 37.7% for dsip, and CSS+DP reduces period by up to 32.1% for tseng. When FFs share PDEs, the general trend is the period gets shorter as more PDEs become available. For k=4, the circuits that never benefit from DP do not differ in per- formance regardless of the number of PDEs available. This is likely because these circuits generally have lower percent of FFs (Table 4.1). This is mostly true for the k=6 case as well. For the circuits that benefit from DP, the k=4 case sees the dsip circuit not having any difference across all configurations, possibly owing to the fact that it has the least FF density of the four circuits. For the k=6 case, elliptic is the circuit with the highest FF density, and the other three circuits do not show much difference in performance as the number of PDEs is increased. In general, the opportunity for delay padding becomes ap- parent only when more PDEs are available for sharing. More PDEs gives skew assignment more flexibility, and makes it more probable for critical edges to occur. For circuits that do not benefit from DP, one PDE per CLB is generally enough for the best solution possible. There are some instances when more PDEs are available for sharing, but the perfor- 63 Chapter 6. Experimental Results and Discussion ci rc ui t 1 P D E 2 P D E 3 P D E 4 P D E 5 P D E 6 P D E 20 P D E CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP bi gk ey 90 .7 92 .2 92 .2 92 .2 92 .2 92 .2 92 .2 cl m a 91 .2 91 .2 91 .2 91 .2 91 .2 91 .2 91 .2 di ffe q 78 .2 78 .3 78 .3 78 .3 78 .3 78 .3 78 .3 ds ip 74 .0 72 .6 73 .6 72 .5 73 .6 72 .5 73 .6 72 .5 73 .6 72 .5 73 .6 72 .5 73 .6 72 .5 el lip ti c 98 .4 98 .2 98 .0 96 .8 95 .5 93 .7 88 .7 91 .1 88 .7 91 .1 68 .1 91 .1 68 .1 fr is c 87 .4 78 .6 79 .8 78 .3 78 .1 78 .6 76 .9 78 .6 76 .9 78 .6 74 .4 78 .6 74 .4 s2 98 99 .7 99 .7 99 .7 99 .7 99 .7 99 .7 99 .7 s3 84 17 97 .4 98 .4 97 .1 97 .7 97 .5 97 .5 97 .5 s3 85 84 .1 88 .5 88 .5 87 .7 87 .7 87 .7 87 .7 87 .7 ts en g 97 .8 79 .2 79 .2 73 .9 79 .2 77 .0 79 .2 67 .9 79 .2 67 .9 79 .2 67 .9 ge om ea n 89 .9 89 .7 87 .3 87 .0 86 .1 86 .8 85 .7 86 .5 84 .6 86 .5 82 .2 86 .5 82 .2 T ab le 6. 1: C SS an d D P R es ul ts , % of O ri gi na l P er io d, k= 4 64 Chapter 6. Experimental Results and Discussion ci rc ui t 1 P D E 2 P D E 3 P D E 4 P D E 5 P D E 6 P D E 7 P D E 20 P D E CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP CSS CSS+DP bi gk ey 65 .8 65 .8 65 .6 65 .6 65 .6 65 .6 65 .6 65 .6 cl m a 96 .9 96 .9 96 .9 96 .9 96 .9 96 .9 96 .9 96 .9 di ffe q 96 .6 89 .2 84 .8 83 .3 84 .8 83 .3 84 .8 83 .3 84 .8 83 .3 84 .8 83 .3 84 .4 83 .3 ds ip 72 .9 62 .6 62 .7 62 .3 62 .3 62 .3 62 .3 62 .3 el lip ti c 98 .8 97 .3 98 .8 95 .8 96 .9 88 .5 85 .4 88 .5 85 .4 88 .5 74 .5 88 .5 74 .5 fr is c 92 .9 82 .0 81 .8 81 .3 77 .7 81 .3 73 .6 81 .3 68 .2 81 .3 68 .2 81 .3 68 .2 81 .3 68 .2 s2 98 10 0 10 0 10 0 10 0 10 0 10 0 10 0 10 0 s3 84 17 99 .3 99 .9 99 .3 98 .3 98 .3 97 .9 97 .9 97 .9 s3 85 84 .1 96 .7 88 .1 88 .1 88 .1 88 .1 88 .1 88 .1 88 .1 ts en g 88 .5 88 .9 76 .6 76 .2 76 .6 74 .7 76 .6 73 .1 76 .6 73 .1 76 .6 73 .1 76 .6 73 .1 ge om ea n 90 .0 86 .0 84 .3 83 .8 83 .9 82 .9 83 .3 81 .0 83 .2 81 .0 83 .2 79 .9 83 .2 79 .9 T ab le 6. 2: C SS an d D P P er fo rm an ce , % of O ri gi na l P er io d, k= 6 65 Chapter 6. Experimental Results and Discussion mance is slightly worse than the case with fewer PDEs. An example is frisc at k=4 for CSS only, from 3-PDEs to 4-PDEs. The difference in skew schedule is caused by a difference in how the PDEs are shared. In other words, the optimum period before the skews are quantized are the same, and the slight difference in skew schedule due to different PDE sharing schemes causes the final period to differ. The difference is less than one percent. For CSS+DP, an example is tseng at k=4, between the 3-PDE and 4-PDE columns. In this case, the ability to complete delay padding differs when PDEs are shared. When there are three PDEs for sharing, the skew schedule may help DP to share PDEs better than when there are four PDEs for sharing. There are also cases where the CSS+DP result is worse than the CSS-only result, for example, frisc at k=4 and 2 PDEs per CLB. While the CSS+DP skew schedule is produced by the circuit graph without all the critical hold edges, the CSS only solution is produced by the graph with those edges. Therefore, the PDE sharing arrangements during the second pass are different, and this results in the final optimum period being different. This difference is also less than one percent. 6.1.2 Power Overhead Table 6.3 and 6.4 show the power overhead, normalized to the power of the original circuit. The first two columns represent power overhead for the architecture where each FF has its own PDE, and expressed as a percentage. The “no PDE” column excludes the power of the PDEs themselves, but includes the power fluctuation due to changes in activity. The increase averages 3-4% for both LUT sizes. When the PDEs’ own power is included, as shown in the “PDE” column, the total power overhead of the scheme becomes apparent. The PDE designed in [19] is used with 6 stages of delay. As a result of more FETs needed as well as progressively larger FETs needed to provide large delays, each PDE adds roughly 66 Chapter 6. Experimental Results and Discussion 45 units of power (45fF, activity of 1). The power overhead is compared to that of the approach used in [28], which uses 4 phase-shifted global clocks. Since [28] does not report power usage, we estimate the power of 3 additional global clocks as follows. For each global clock, the placement size (rows × columns of CLBs) is obtained from VPR, and it is assumed that CLBs are laid over a spine-and-ribs clock network. For example, if a circuit is 10×10 CLBs, then the power due to one extra clock is Cclk = 10 ·Crib+Cspine+Cclb, and Crib = 10 ·Cint, Cspine = 10 ·Cglobal, and Cclb = 10×10 ·Clocal ·%FF . Cglobal, Cint and Clocal are the capacitance of the wire that spans the length of 1 CLB for global, intermediate and local type routing wire, calculated from the interconnect model and typical wire dimensions from [3]. %FF is the percent of the CLBs that contain an active flip-flop. The power overhead of 3 more global clocks is then 3 ·Cclk. This is shown in the 4-clk column. Bigger circuits with fewer user FFs incur less overhead with our approach, while small circuits with more SEs favor the approach in [28]. Power overhead can be decreased by using fewer PDEs as shown in the other columns. In these cases, the power overhead further includes the capacitance of the wires needed to deliver each delayed signal into the CLB. This overhead is around 18fF for a 125µm segment of local routing, which is relatively small compared to the PDE’s capacitance. For both LUT sizes, all circuits and all configurations of PDE sharing, the power overhead is smaller than that with the original 1-PDE-per-FF approach and the 4-clk approach due to multiple FFs sharing the same PDE in many CLBs. For some circuits, the power overhead increases as more PDEs became available and are used for sharing, but the performance does not necessarily increase. For example, diffeq ’s period is the same (k=4) in all configurations (Table 6.1), but the power overhead is always increasing (Table 6.3). This demonstrates the fact that there are many skew schedules that can satisfy a 67 Chapter 6. Experimental Results and Discussion circuit no P D E P D E 4- cl k (e st im at ed ) 1 P D E 2 P D E 3 P D E 4 P D E 5 P D E 6 P D E bigkey 103 162 287 149 163 163 163 163 163 clma 106 119 206 114 119 119 119 119 119 diffeq 99.3 636 567 358 474 529 534 534 534 dsip 100 153 264 144 146 146 146 146 146 elliptic 104 235 193 153 168 175 181 181 210 frisc 105 618 557 319 408 431 452 459 482 s298 111 114 168 114 114 114 114 114 114 s38417 102 168 167 123 133 150 154 156 157 s38584.1 102 156 165 131 141 146 147 147 147 tseng 99.8 391 332 237 274 298 292 329 329 geomean 103 224 261 169 188 196 198 201 205 Table 6.3: Power after CSS and DP, % of Original Dynamic Power, k = 4 circuit no P D E P D E 4- cl k (e st im at ed ) 1 P D E 2 P D E 3 P D E 4 P D E 5 P D E 6 P D E 7 P D E bigkey 101 182 348 162 172 177 179 179 179 179 clma 101 118 191 118 118 118 118 118 118 118 diffeq 99.6 812 475 367 450 546 578 586 590 590 dsip 100 172 316 154 158 161 161 161 161 161 elliptic 108 275 209 151 164 167 170 195 196 226 frisc 103 736 571 332 411 447 476 539 545 545 s298 107 111 156 111 111 111 111 111 111 111 s38417 113 261 199 151 172 182 200 207 220 224 s38584.1 102 174 178 134 146 151 152 152 153 153 tseng 104 463 384 264 286 338 336 370 370 370 geomean 104 260 276 178 194 206 212 220 222 226 Table 6.4: Power after CSS and DP, % of Original Dynamic Power, k = 6 68 Chapter 6. Experimental Results and Discussion 0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 40 200 400 600 800 N um D el ay s 0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 40 200 400 600 800 Delay (ns) N um D el ay s k = 4 k = 6 Figure 6.1: Skew Histogram given period in the decision version of the CSS problem. Those that can achieve the same period with fewer number of distinct skews are better in terms of power. Running each benchmark across a range of configurations, one can determine what is the best sharing scheme for individual circuits. A cumulative histogram of the total number of PDEs used for each discrete delay across all circuits is shown in Figure 6.1. The data is relatively spread out, indicating that PDEs use a large number of different delays. 6.1.3 Area Overhead Area calculation is done with the model used in [19] and a channel width of 200. With 1 PDE assigned to each FF, 20 PDEs are needed per CLB, and the overhead is 11.7% for k=4 and 7.6% for k=6. This is the maximum area overhead with our approach. With PDE sharing, the area over decreases drastically. For example, a 4 PDE configuration will cut the overhead by nearly 5x, taking into account the area of 20 multiplexers for every FF. 69 Chapter 6. Experimental Results and Discussion 6.2 GlitchLess Only Results In Figure 6.2, normalized glitching power is plotted against the ranking threshold per- centage, where the node with the maximum glitching power in each circuit is defined as a threshold of 1.0. The design flow follows choice “1” in Figure 4.3. PDE overhead is not added. The lines represent glitch power savings as the threshold is gradually decreased and more nets are de-glitched. The trend is rather gradual for threshold values larger than 20%, and this makes sense since there are only a few nodes with high glitching power consumption. As the threshold is lowered below 20%, more nodes became eligible for skew assignment and the rate of savings increases more dramatically. The square and circle lines show that even at a threshold of 0, total glitch elimination is not possible because nodes on the critical path are not considered for de-glitching. When these nodes are considered, the PDEs result in an increase of the critical path, allowing all glitching to be removed, as the triangle and cross lines point out. The difference in power savings between the cases when there is only 1 PDE per CLB, and when every FF has its own PDE is almost negligible for threshold values greater than 0.2. This is shown in Figure 6.3 by the additional curves. This is because the few nodes that have glitching power higher than a threshold of 0.2 are very likely to be spread out among many CLBs, instead of concentrating inside a single CLB. However, as the threshold is further decreased, more nodes in every CLB will become eligible for GlitchLess, and the savings increases. Lastly, an additional configuration where there are 2 PDEs given to each CLB for GlitchLess, is shown to have negligible difference from the square and circle lines. The impact of increasing the critical path is shown in Figure 6.4. Again, PDE overhead is omitted. The Y-axis is a product of the dynamic power and the period, normalized to that of the original circuit placed and routed by VPR. The square lines excludes nodes on the critical path, so period stays the same as they reflect potential power savings. However, 70 Chapter 6. Experimental Results and Discussion 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 k = 4 G litc h Po we r excludes crit. path nodes includes crit. path nodes 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 k = 6 G litc h Po we r excludes crit. path nodes includes crit. path nodes Figure 6.2: Glitch Power Reduction, 1 PDE per FF 71 Chapter 6. Experimental Results and Discussion 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.3 0.4 0.5 0.6 0.7 0.8 0.9 1 k = 4 G litc h Po we r excludes crit. path nodes, 1 PDE per CLB excludes crit. path nodes, 2 PDE per CLB excludes crit. path nodes 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.3 0.4 0.5 0.6 0.7 0.8 0.9 1 k = 6 G litc h Po we r excludes crit. path nodes, 1 PDE per CLB excludes crit. path nodes, 2 PDE per CLB excludes crit. path nodes Figure 6.3: Glitch Power Reduction, PDE Sharing 72 Chapter 6. Experimental Results and Discussion it is clear from the other curve that the additional power savings from doing GlitchLess on critical path nodes is not worth the performance impact it incurs, as the power-period product goes above 1.0, and is never better than the case where critical path is not allowed to increase. Figure 6.5 shows the power-period product plots when PDE overhead is added, and Figure 6.6 shows the number of PDEs used on a log scale. Roughly a third of the savings can be obtained using less than 10 PDEs on average, for both k=4 and 6, confirming that there are indeed very few nodes with high glitching power. When the critical path is allowed to increase, more PDEs are needed. As the threshold is lowered below 20%, the number of PDEs used increases dramatically in all three cases, and the savings from the diminishing rate of return of each added PDE is over-compensated by the PDE’s own power overhead. As a result, savings at low thresholds are gone. It is interesting to see that for threshold values below 0.3, the 1-PDE-per-CLB configuration yields slightly better power savings because it limits the number of PDEs used. The 2-PDE-per-CLB configuration needs twice as many PDEs to be inserted into the architecture, but only improves the averages by a negligible amount for k=4, and not at all for k=6. This means having 1 PDE per CLB is good enough for the benchmark suite used for this thesis. The best savings (Pfinal for 1-PDE-per-FF, Pfinal1PDE for 1-PDE-per-CLB) for each circuit, where period is not allowed to increase, is presented in Table 6.5. The limit (Plimit) refers to the best possible savings achievable (assuming 100% glitch elimination with no PDE overhead), and the percentage of the limit achieved is also shown (%). Some circuits cannot be improved, so their limiting case is close to 100% because glitching is only a small fraction of total dynamic power. There are several cases (shown in bold) where the result from 1-PDE-per-CLB is better than 1-PDE-per-FF due to multiple nodes sharing the same PDE. Overall, the technique is able to remove on average 13% to 20% of glitching power, 73 Chapter 6. Experimental Results and Discussion 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 k = 4 D yn am ic Po we r * P er io d excludes crit. path nodes includes crit. path nodes 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.94 0.95 0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 k = 6 D yn am ic Po we r * P er io d excludes crit. path nodes includes crit. path nodes Figure 6.4: Power Plot Without PDE Overhead 74 Chapter 6. Experimental Results and Discussion 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.96 0.97 0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 k = 4 D yn am ic Po we r * P er io d includes crit. path nodes excludes crit. path nodes excludes crit. path nodes, 2 PDE per CLB excludes crit. path nodes, 1 PDE per CLB 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.98 0.99 1 1.01 1.02 1.03 1.04 1.05 k = 6 D yn am ic Po we r * P er io d includes crit. path nodes excludes crit. path nodes excludes crit. path nodes, 2 PDE per CLB excludes crit. path nodes, 1 PDE per CLB Figure 6.5: Power Plot With PDE Overhead 75 Chapter 6. Experimental Results and Discussion 0 0.2 0.4 0.6 0.8 110 −1 100 101 102 103 104 k = 4 N um be r o f P DE s Us ed includes crit. path nodes excludes crit. path nodes excludes crit. path nodes, 2 PDE per CLB excludes crit. path nodes, 1 PDE per CLB 0 0.2 0.4 0.6 0.8 110 −1 100 101 102 103 k = 6 N um be r o f P DE s Us ed includes crit. path nodes excludes crit. path nodes excludes crit. path nodes, 2 PDE per CLB excludes crit. path nodes, 1 PDE per CLB Figure 6.6: PDE Usage for GlitchLess 76 Chapter 6. Experimental Results and Discussion ci rc ui t k = 4 k = 6 P final % P final1PDE % P final2PDE % P limit P final % P final1PDE % P final2PDE % P limit bi gk ey 10 0 0 10 0 0 10 0 0 98 .2 10 0 0 10 0 0 10 0 0 98 .4 cl m a 98 .0 32 .3 98 .2 29 .0 98 .1 30 .6 93 .8 98 .6 23 .0 98 .3 27 .9 98 .4 26 .2 93 .9 di ffe q 10 0 0 10 0 0 10 0 0 96 .6 10 0 0 10 0 0 10 0 0 97 .9 ds ip 10 0 0 10 0 0 10 0 0 98 .8 10 0 0 10 0 0 10 0 0 98 .6 el lip ti c 86 .7 63 .3 87 .5 59 .5 86 .7 63 .3 79 .0 92 .2 49 .1 92 .2 49 .1 92 .0 50 .3 84 .1 fr is c 94 .6 41 .5 94 .6 41 .5 94 .6 41 .5 87 .0 95 .1 36 .6 95 .1 36 .6 95 .1 36 .6 86 .6 s2 98 10 0 0 10 0 0 10 0 0 78 .3 98 .3 7. 3 98 .3 7. 3 98 .3 7. 3 76 .8 s3 84 17 85 .7 53 .4 85 .7 53 .4 85 .7 53 .4 73 .2 98 .8 8. 3 96 .6 23 .6 97 .2 19 .4 85 .6 s3 85 84 .1 99 .5 6. 4 98 .6 17 .9 98 .6 17 .9 92 .2 99 .6 10 .0 99 .6 10 .0 99 .6 10 .0 96 .0 ts en g 99 .6 2. 6 99 .6 2. 6 99 .6 2. 6 84 .8 10 0 0 10 0 0 10 0 0 91 .6 av er ag e 20 .0 20 .4 20 .9 13 .4 15 .4 15 .0 ge om ea n 96 .9 96 .3 96 .2 87 .7 98 .2 98 .0 98 .0 90 .7 T ab le 6. 5: D yn am ic P ow er af te r G lit ch L es s 77 Chapter 6. Experimental Results and Discussion or up to 63% for individual circuits. The Pfinal2PDE column represents the savings for the 2-PDE-per-CLB scheme, and the performance is almost identical to the 1-PDE-per-CLB scheme. 6.3 GlitchLess Savings After CSS+DP+GL Run Choice “3” in Figure 4.3 offers the complete flow. This section shows the effectiveness of GlitchLess with prior CSS perturbation on glitching activity. Table 6.6 provides GlitchLess results after CSS+DP+GlitchLess are run with 4 PDEs per CLB. This particular scheme is chosen because 4 PDEs per CLB is enough for most circuits to reach the same period reduction obtainable via the 1-FF-per-PDE scheme. Of the 4 PDEs, GlitchLess is allowed to use at most 1 free PDE, since in the last section it is shown using additional PDEs does not result in additional savings. To compare against GlitchLess only results in the last section, the power overhead of CSS and DP PDEs are not included to be consistent (CSS and DP are not done for GlitchLess only tool flow). For example, if a CLB has 2 PDEs used for CSS and 1 PDE used for GlitchLess, the dynamic power numbers presented in Table 6.6 only takes into account the 1 PDE used for GlitchLess and glitching power generated by all nodes inside the CLB. In Table 6.6, the Pinitial column shows the power due to functional and glitching ac- tivity (excluding CSS/DP PDEs) after CSS+DP is performed. The Pfinal column shows the power after GlitchLess, including functional/glitching activity and GlitchLess PDE overhead. The Plimit represent the best possible savings, similar to Table 6.5. The average percent of glitching power eliminated is 18% and 15% for k=4 and 6. This is on par with the corresponding averages from the GlitchLess results (20% and 13%). 78 Chapter 6. Experimental Results and Discussion circuit k = 4 k = 6 Pinitial Pfinal % Plimit Pinitial Pfinal % Plimit bigkey 102.5 102.5 0 98.2 100.8 100.8 0 98.4 clma 105.9 100.5 44.6 93.8 101.2 98.9 31.5 93.9 diffeq 99.3 99.3 0 96.6 99.5 99.5 0 97.9 dsip 100.4 100.4 0 98.8 100.4 100.4 0 98.6 elliptic 104.3 89.0 60.5 79.0 100.9 93.5 44.0 84.1 frisc 105.2 99.3 32.4 87.0 103.6 99.0 27.1 86.6 s298 111.1 110.8 0.9 78.3 106.9 105.6 4.3 76.8 s38417 102.1 91.5 36.7 73.2 111.0 103.2 30.7 85.6 s38584.1 101.8 101.2 6.2 92.2 101.7 101.2 8.8 96.0 tseng 102.5 102.5 0 84.8 104.0 104.0 0 91.6 average 18.1 14.6 geomean 103.6 101.8 87.7 102.9 101.7 90.7 Table 6.6: Dynamic Power after Full Run, Excluding CSS+DP PDE Overhead 6.4 Summary of Results This chapter detailed the performance and power improvements for doing CSS+DP only and for GlitchLess only. CSS alone reduces period by 13% and 17% on average for k=4 and 6, respectively, and up to 37.7% for individual circuits. Delay Padding further benefits four circuits for both LUT sizes, and the average improvement is 18% and 20% of the original period. The average period reduction for all of the circuits that benefit from DP is an additional 10% of the original period on top of the CSS only results, and up to an additional 23% of the original period of individual circuits. Power overhead due to PDEs used for CSS and DP is significant. The power overhead after CSS and DP are done, expressed as a percentage of the original power, is 124% and 160% for k=4 and 6, respectively. PDE sharing helps to reduce the power overhead, but reduces the improvement on period. Usually, the circuits that have lower FF density benefit more from PDE sharing without having to give up performance improvements. For the circuits that benefit from 79 Chapter 6. Experimental Results and Discussion delay padding, PDE sharing takes away the flexibility to assign every FF in the CLB with a different skew, and the penalty on performance improvements is more severe. On average, a 1-PDE-per-CLB scheme gives on average 10% period reduction for both k values, and the power overhead is 69% and 78% for k=4 and 6, respectively. To retain all of the performance improvements, 6 and 7 PDEs need to be available for sharing for k=4 and 6. The power overhead in this case is 105% and 126%. The power overhead of a previous approach that used 4 global clocks to provide skews is estimated to be 161% and 176% for k=4 and 6, and is on the same order of magnitude as our approach. For GlitchLess only, the percentage of glitching power that can be reduced averages at 70% and 60% for k=4 and 6. This translates to an average dynamic power reduction of 9% and 6% without PDE power overhead. In order to achieve 100% glitching elimination, the critical path must be allowed to increase, but this performance penalty is not worth the extra power benefit. Including PDE overhead, 13% to 20% of glitching power can be reduced on average, and up to 63% for individual circuits. The power savings for the 1-PDE and 2-PDE per CLB schemes are almost identical to the 1-PDE-per-FF scheme. For full run results including CSS, DP and GlitchLess, average glitch elimination of 18% and 15% is achieved for k=4 and 6, and this is on par with the averages obtained in the GlitchLess only case. 80 Chapter 7 Conclusions and Future Work 7.1 Conclusions This thesis presented an architecture change and associated tool flow to consider both power and performance optimization. A programmable delay element (PDE) added to each FF clock input can be used to satisfy CSS, delay padding and GlitchLess simultaneously. The combined CSS and DP algorithm uses iterative binary search to repeatedly relax hold-time constraints and reduce the period. CSS alone can improve period by an average of 13% and 17% of the original period for k=4 and 6, respectively. With CSS and DP, the average improvement is 18% and 20% of the original period. The average period reduction for the circuits that benefit from DP is 10% of the original period. When there is a restriction on the number of PDEs in each CLB, it is found that a maximum of 7 PDEs per CLB is sufficient to achieve the same result as when each FF has access to a dedicated PDE. However, most circuits that cannot benefit from DP achieve maximum optimization with just one or two PDEs per CLB. PDE power consumption overhead with CSS is significant and is an estimated average of 124% (k=4) and 160% (k=6) of the original circuit’s dynamic power. This is on par with previous work that used 4 global clocks to provide skews. When there is a restriction on the number of PDEs each CLB can share, the power overhead can be reduced to an average of 70% for both LUT sizes when each CLB is restricted to share just 1 PDE. 81 Chapter 7. Conclusions and Future Work For glitch reduction, the modified GlitchLess traverses the circuit in a depth-first fash- ion, assigning PDEs only to nodes with glitching power that is higher than user specified threshold. On average, it can reduce 13% to 20% of glitching power, and up to 63% for in- dividual circuits. The average is low because, for several circuits, nodes with high glitching power are on critical paths and cannot tolerate increased delays. Also, the PDEs consume significant power, making it impractical to apply GlitchLess to every circuit node. Finally, for a full run, including CSS, DP and Glitchless, the average percentage of glitching elimi- nated is 15% to 18%, and this is on par with the averages obtained in the GlitchLess only case. To provide accurate glitch estimation, the modified ACE algorithm uses simulation results from Cadence combined with glitch pulse-width calculations and binning. The results showed that the original method can underestimate glitching by as much as 48% while overestimating it by up to 15%. 7.2 Future Work The MCNC circuits used in this thesis are small circuits that are more than 15 years old, and take up a small area of at most 30 x 30 CLBs. Larger and more modern circuits will provide a better measurement of the effectiveness of CSS, DP and GlitchLess. Since most modern circuits contain memory (RAM) and digital signal processing (DSP) functional blocks, the nature of modern circuits is significantly different. Therefore, once modern circuits are obtained, one will need to change ACE to simulate RAM/DSP blocks and to modify the data structures in PGR to include multi-output nodes. Chapter 6 showed that PDE overhead incurs a large power overhead with CSS and DP, and it limits GlitchLess performance. Therefore, it is important to have a power-efficient PDE to drive down the cost of implementing CSS and DP. 82 Chapter 7. Conclusions and Future Work In terms of algorithm design, PGR can potentially be improved in the following areas. 7.2.1 CSS During CSS, the quantization process after each iteration of the binary search adjusts each skew to the nearest delay value. This minimizes the difference between the adjusted skew and the original (continuous) skew. However, a pair of skews (for example, xj and xi) may be adjusted in a way such that the period needs to be increased to satisfy the setup constraints. An alternative way to do skew adjustment is a random “snapping” of skews. For example, a skew of 0.2345 would be randomly adjusted to 0.2 or 0.3, with a discretization level of 0.1. This approach should be implemented to compare to the existing approach. During the second pass of CSS+DP when CLBs are restricted to PDE sharing, the FFs are randomly distributed to PDEs for CLBs that needed more distinct skew values in the first pass than the number of available PDEs. This simple solution is easier to implement but may not produce the best impact on the period. There may be other sharing schemes that can even further reduce the power overhead while minimizing the impact on period. For example, the number of PDEs required may be reduced by extending the optional two-pass approach into a multi-pass algorithm, regardless of whether the number of PDEs per CLB satisfies the optimum skew schedule from the first pass or not. The reason to do many passes is based on the fact that there are many clock schedules that can satisfy the same period. After each pass, the algorithm can check each CLB to see if there are PDEs that have the same skew. If found, the FFs that share these PDEs can be further combined into a “larger” node in the CSS graph, and the CSS+DP procedure is repeated. This may be more beneficial as the number of PDEs shared by each CLB increases. 83 Chapter 7. Conclusions and Future Work 7.2.2 Glitch Estimation When a node is assigned a GlitchLess PDE, its arrival time changes due to timing margins, and its output activity changes due to glitch elimination. This will affect the glitching cre- ated on downstream nodes. Since PGR and ACE are separate, standalone tools, it is not feasible to update activity estimation after GlitchLess is done on every node. Therefore, it is beneficial to integrate ACE into the PGR/VPR framework, so that not only glitch prop- agation can be done (Chapter 3.5), but glitch estimation can be updated more frequently to provide better ranking for each logic level of the circuit. 84 Bibliography [1] Sequential Interactive Synthesis (SIS) abstract page, 1994. http://embedded.eecs. berkeley.edu/research/sis/abstract.html. [2] iFAR - intelligent FPGA architecture repository, 2008. http://www.eecg.utoronto. ca/vpr/architectures/. [3] Predictive technology model (PTM) website, 2008. http://www.eas.asu.edu/~ptm/. [4] Christoph Albrecht. Efficient incremental clock latency scheduling for large circuits. In Conference on Design, Automation and Test in Europe, pages 1091–1096, Munich, Germany, 2006. European Design and Automation Association. [5] Altera Corporation. Achieving low power in 65-nm Cyclone III FPGAs, 2007. http: //www.altera.com/literature/wp/wp-01016.pdf. 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Title | Period and glitch reduction via clock skew scheduling, delay padding and glitchless |
Creator |
Dong, Xiao |
Publisher | University of British Columbia |
Date Issued | 2009 |
Description | This thesis describes PGR, an architectural technique to reduce dynamic power via a glitch reduction strategy named GlitchLess, or to improve performance via clock skew scheduling (CSS) and delay padding (DP). It is integrated into VPR 5.0, and is invoked after the routing stage. Programmable delay elements (PDEs) are used as a novel architecture modification to insert delay on fiip- flop (FF) clock inputs, enabling all optimization steps to share it, avoiding multiple architecture modifications. This thesis investigates the trade- off between power and performance, and finding an appropriate compromise considering process variation and timing uncertainties. To facilitate realistic power estimates, a popular activity estimator, ACE, is modified with a new model to estimate glitching power, taking into account the analog behavior of glitch pulse width reduction as it travels along FPGA routing tracks. We show that the original glitch estimation method can underestimate glitching power by up to 48%, and overestimate by up to 15%. In terms of performance, an average of 15% speedup can be achieved via CSS alone, or up to 37% for individual circuits. Although delay padding only benefits a few circuits, the average improvement of those circuits is an additional 10% of the original period, or up to 23% for individual circuits. In addition, GlitchLess is performed on both the original VPR and post-CSS solutions. On average, 16% of glitching power can be eliminated, or up to 63% for individual circuits. |
Extent | 978807 bytes |
Genre |
Thesis/Dissertation |
Type |
Text |
FileFormat | application/pdf |
Language | eng |
Date Available | 2009-09-17 |
Provider | Vancouver : University of British Columbia Library |
Rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
DOI | 10.14288/1.0067710 |
URI | http://hdl.handle.net/2429/12869 |
Degree |
Master of Applied Science - MASc |
Program |
Electrical and Computer Engineering |
Affiliation |
Applied Science, Faculty of Electrical and Computer Engineering, Department of |
Degree Grantor | University of British Columbia |
GraduationDate | 2009-11 |
Campus |
UBCV |
Scholarly Level | Graduate |
Rights URI | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
AggregatedSourceRepository | DSpace |
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