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Pan, Yuxuan

University of British Columbia

Routing is a challenging stage of the Integrated Circuit (IC) design process. A routing algorithm often adopts the two-stage approach of global routing followed by detailed routing. One of the routing objectives is the routability, which requires completing all the required connections without causing routing overflows or wireshorts. Otherwise, the chip would not function well and may even fail. Moreover, detours need to be taken to avoid overflows and wire-shorts, which may increase the wire length and number of vias in the physical design, affecting the overall performance of the circuit. Predicting the existence and locations of routing overflows and wire-shorts before routing takes place helps the router to improve the routability and circuit performance. Here, we present two Machine Learning (ML) techniques that improve the routability of routing by predicting the number and locations of overflows and wire-shorts. First, we design and develop GlobalNet, a Fully Convolutional Network (FCN) based global routing congestion predictor that estimates the density of wires and vias of global routing in 3-Dimensional (3D) from a placed netlist. The locations of overflows are derived from the prediction result. A global router is also implemented to utilize the congestion estimation result to improve the performance of global routing. Second, a Convolutional Neural Network (CNN) based wire-short predictor, VioNet, is developed. VioNet replaces the global router with global routing congestion estimation (GlobalNet) so that the runtime is significantly reduced. To improve the prediction accuracy, we adopt a top-down iterative strategy where a low-resolution prediction first gives the approximate locations of wire-shorts, followed by a high-resolution prediction which determines the precise wire-shorts’ locations. Experimental results show that both GlobalNet and VioNet achieve high accuracy on ISPD 2018 and ISPD 2019 benchmarks. Moreover, UBC-GR increases the routability of global routing by reducing the number of overflows.

Text

2022-08-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82445

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/