Energy-Efficient Stochastic Computing for Convolutional Neural Networks by Using Kernel-wise Parallelism

Zaipeng Xie, Chenyu Yuan, Likun Li, JiaHao Wu
{"title":"Energy-Efficient Stochastic Computing for Convolutional Neural Networks by Using Kernel-wise Parallelism","authors":"Zaipeng Xie, Chenyu Yuan, Likun Li, JiaHao Wu","doi":"10.1109/ISCAS46773.2023.10181378","DOIUrl":null,"url":null,"abstract":"Stochastic computing (SC) is a low-cost computation paradigm that can replace conventional binary arithmetic to provide a low hardware footprint with high scalability. However, since the SC bitstream length grows with the precision of the represented data, regardless of its lower power consumption, the convolutional SC-based neural networks may not be efficient in hardware area and energy. This work proposes a novel SC accelerator, PSC-Conv, to implement the convolutional layer using a new binary-interfaced stochastic computing architecture. PSC-Conv exploits kernel-wise parallelism in CNNs, reducing hardware footprint and energy consumption. Experimental re-sults show that the proposed implementation excels among several state-of-the-art SC-based implementations regarding area and power efficiency. We also compared the implementations of three modern CNNs, including LeNet-5, MobileNet, and ResNet-50. Experimental results demonstrate that, on average, PSC-Conv can achieve 5.02x speedup and 87.9% energy reduction compared with the binary implementation.","PeriodicalId":177320,"journal":{"name":"2023 IEEE International Symposium on Circuits and Systems (ISCAS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Symposium on Circuits and Systems (ISCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS46773.2023.10181378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Stochastic computing (SC) is a low-cost computation paradigm that can replace conventional binary arithmetic to provide a low hardware footprint with high scalability. However, since the SC bitstream length grows with the precision of the represented data, regardless of its lower power consumption, the convolutional SC-based neural networks may not be efficient in hardware area and energy. This work proposes a novel SC accelerator, PSC-Conv, to implement the convolutional layer using a new binary-interfaced stochastic computing architecture. PSC-Conv exploits kernel-wise parallelism in CNNs, reducing hardware footprint and energy consumption. Experimental re-sults show that the proposed implementation excels among several state-of-the-art SC-based implementations regarding area and power efficiency. We also compared the implementations of three modern CNNs, including LeNet-5, MobileNet, and ResNet-50. Experimental results demonstrate that, on average, PSC-Conv can achieve 5.02x speedup and 87.9% energy reduction compared with the binary implementation.
基于核并行性的卷积神经网络节能随机计算
随机计算(SC)是一种低成本的计算范式,可以取代传统的二进制算法,提供低硬件占用和高可扩展性。然而,由于SC比特流长度随着表示数据的精度而增长,而不考虑其较低的功耗,基于卷积SC的神经网络可能在硬件面积和能量方面效率不高。这项工作提出了一种新的SC加速器PSC-Conv,使用一种新的二进制接口随机计算架构来实现卷积层。PSC-Conv利用cnn的核并行性,减少硬件占用和能耗。实验结果表明,所提出的实现在面积和功率效率方面优于几种最先进的基于sc的实现。我们还比较了三种现代cnn的实现,包括LeNet-5、MobileNet和ResNet-50。实验结果表明,与二进制实现相比,PSC-Conv实现的平均速度提高5.02倍,能耗降低87.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信