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research-article

FP-BNN

Authors:

Shaojun WeiAuthors Info & Claims

Neurocomputing, Volume 275, Issue C

Pages 1072 - 1086

https://doi.org/10.1016/j.neucom.2017.09.046

Published: 31 January 2018 Publication History

Abstract

Deep neural networks (DNNs) have attracted significant attention for their excellent accuracy especially in areas such as computer vision and artificial intelligence. To enhance their performance, technologies for their hardware acceleration are being studied. FPGA technology is a promising choice for hardware acceleration, given its low power consumption and high flexibility which makes it suitable particularly for embedded systems. However, complex DNN models may need more computing and memory resources than those available in many current FPGAs. This paper presents FP-BNN, a binarized neural network (BNN) for FPGAs, which drastically cuts down the hardware consumption while maintaining acceptable accuracy. We introduce a Resource-Aware Model Analysis (RAMA) method, and remove the bottleneck involving multipliers by bit-level XNOR and shifting operations, and the bottleneck of parameter access by data quantization and optimized on-chip storage. We evaluate the FP-BNN accelerator designs for MNIST multi-layer perceptrons (MLP), Cifar-10 ConvNet, and AlexNet on a Stratix-V FPGA system. An inference performance of Tera opartions per second with acceptable accuracy loss is obtained, which shows improvement in speed and energy efficiency over other computing platforms.

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https://dl.acm.org/doi/10.1145/3636534.3649384
Yang GLei JFang ZLi YZhang JXie W(2024)HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural NetworksACM Transactions on Reconfigurable Technology and Systems10.1145/363161017:2(1-24)Online publication date: 30-Apr-2024
https://dl.acm.org/doi/10.1145/3631610
He YZhang LWu WZhou H(2024)Binarizing by Classification: Is Soft Function Really Necessary?IEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.328857234:2(973-982)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3288572
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Published In

cover image Neurocomputing

Neurocomputing Volume 275, Issue C

January 2018

2070 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 31 January 2018

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Cited By

Yang XAn ZPan QYang LLei DFan YGanesan DShi W(2024)Binary Optical Machine Learning: Million-Scale Physical Neural Networks with Nano NeuronsProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649384(603-617)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3636534.3649384
Yang GLei JFang ZLi YZhang JXie W(2024)HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural NetworksACM Transactions on Reconfigurable Technology and Systems10.1145/363161017:2(1-24)Online publication date: 30-Apr-2024
https://dl.acm.org/doi/10.1145/3631610
He YZhang LWu WZhou H(2024)Binarizing by Classification: Is Soft Function Really Necessary?IEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.328857234:2(973-982)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3288572
Song MAsim FLee JKim T(2024)Extending Neural Processing Unit and Compiler for Advanced Binarized Neural NetworksProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473822(115-120)Online publication date: 22-Jan-2024
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Rangsikunpum AAmiri SOst L(2024)A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge DevicesIET Computers & Digital Techniques10.1049/cdt2/62144362024Online publication date: 1-Jan-2024
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Liu FLi HHu WHe Y(2024)Review of neural network model acceleration techniques based on FPGA platformsNeurocomputing10.1016/j.neucom.2024.128511610:COnline publication date: 28-Dec-2024
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Huang CChen YHsu CYang JChang C(2024)FPGA-based UAV and UGV for search and rescue applicationsComputers and Electrical Engineering10.1016/j.compeleceng.2024.109491119:PAOnline publication date: 1-Oct-2024
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Al-Rikabi HRenczes B(2024)Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural NetworksJournal of Signal Processing Systems10.1007/s11265-024-01911-096:4-5(301-312)Online publication date: 1-May-2024
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Susskind ZArora AMiranda IBacellar AVillon LKatopodis Rde Araújo LDutra DLima PFrança FBreternitz Jr. MJohn L(2023)ULEEN: A Novel Architecture for Ultra-low-energy Edge Neural NetworksACM Transactions on Architecture and Code Optimization10.1145/362952220:4(1-24)Online publication date: 25-Oct-2023
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