MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
department or school: Applied Science, Faculty of
department or school: Non UBC
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
identifier citation: Applied Sciences 12 (1): 89 (2022)
creator: Chen, Ruiqi
creator: Wu, Tianyu
creator: Zheng, Yuchen
creator: Ling, Ming
issued: 2022-01-27T16:07:28Z
issued: 2021-12-22
description: In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/80740
note: Citation: Chen, R.; Wu, T.; Zheng, Y.;Ling, M. MLoF: Machine LearningAccelerators for the Low-Cost FPGAPlatforms. Appl. Sci. 2022, 12, 89.https://doi.org/10.3390/app12010089Academic Editors: Deliang Fan andZhezhi HeReceived: 2 November 2021Accepted: 16 December 2021Published: 22 December 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).applied sciencesArticleMLoF: Machine Learning Accelerators for the Low-CostFPGA PlatformsRuiqi Chen 1 , Tianyu Wu 1,2 , Yuchen Zheng 3 and Ming Ling 4,*1 VeriMake Innovation Lab, Nanjing Renmian Integrated Circuit Co., Ltd., Nanjing 210088, China;rickychen@verimake.com (R.C.); twu15@ece.ubc.ca (T.W.)2 Department of Electrical and Computer Engineering, University of British Columbia,Vancouver, BC V6T 1Z4, Canada3 Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA;felix02@email.unc.edu4 National ASIC System Engineering Technology Research Center, Southeast University, Nanjing 210096, China* Correspondence: trio@seu.edu.cnAbstract: In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML)algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, andhigh-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IPcores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achievecomprehensive performance in various tasks. With Verilog, we deploy and accelerate ArtificialNeural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support VectorMachines (SVMs) on 10 different FPGA development boards from seven producers. Additiona…
has type: Article
is shown at: 10.14288/1.0406365
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/app12010089
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: Internet of Things
subject: machine learning
subject: embedded system
subject: FPGA
subject: hardware accelerator
type: Text
identifier URI: http://hdl.handle.net/2429/80740