Spiker+: A Framework for the Generation of Efficient Spiking Neural Networks FPGA Accelerators for Inference at the Edge

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Alessio Carpegna;Alessandro Savino;Stefano Di Carlo
{"title":"Spiker+: A Framework for the Generation of Efficient Spiking Neural Networks FPGA Accelerators for Inference at the Edge","authors":"Alessio Carpegna;Alessandro Savino;Stefano Di Carlo","doi":"10.1109/TETC.2024.3511676","DOIUrl":null,"url":null,"abstract":"Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the network periphery, containing sensitive data within the boundaries of the edge device. This facilitates real-time decision-making, reduces latency and power consumption, and enhances privacy and security. Spiking Neural Networks (SNNs) offer a promising computing paradigm in these environments. However, deploying efficient SNNs in resource-constrained edge devices requires highly parallel and reconfigurable hardware implementations. We introduce Spiker+, a comprehensive framework for generating efficient, low-power, and low-area SNN accelerators on Field Programmable Gate Arrays for inference at the edge. Spiker+ presents a configurable multi-layer SNN hardware architecture, a library of highly efficient neuron architectures, and a design framework to enable easy, Python-based customization of accelerators. Spiker+ is tested on three benchmark datasets: MNIST, Spiking Heidelberg Dataset (SHD), and AudioMNIST. On MNIST, it outperforms state-of-the-art SNN accelerators in terms of resource allocation, with a requirement of 7,612 logic cells and 18 Block RAMS (BRAMs), and power consumption, draining only 180 mW, with comparable latency (780 <inline-formula><tex-math>$\\mu$</tex-math></inline-formula>s/img) and accuracy (97%). On SHD and AudioMNIST, Spiker+ requires 18,268 and 10,124 logic cells, respectively, requiring 51 and 16 BRAMs, consuming 430 mW and 290 mW, with an accuracy of 75% and 95%. These results underscore the significance of Spiker+ in the hardware-accelerated SNN landscape, making it an excellent solution for deploying configurable and tunable SNN architectures in resource and power-constrained edge applications.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"784-798"},"PeriodicalIF":5.4000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10794606","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10794606/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the network periphery, containing sensitive data within the boundaries of the edge device. This facilitates real-time decision-making, reduces latency and power consumption, and enhances privacy and security. Spiking Neural Networks (SNNs) offer a promising computing paradigm in these environments. However, deploying efficient SNNs in resource-constrained edge devices requires highly parallel and reconfigurable hardware implementations. We introduce Spiker+, a comprehensive framework for generating efficient, low-power, and low-area SNN accelerators on Field Programmable Gate Arrays for inference at the edge. Spiker+ presents a configurable multi-layer SNN hardware architecture, a library of highly efficient neuron architectures, and a design framework to enable easy, Python-based customization of accelerators. Spiker+ is tested on three benchmark datasets: MNIST, Spiking Heidelberg Dataset (SHD), and AudioMNIST. On MNIST, it outperforms state-of-the-art SNN accelerators in terms of resource allocation, with a requirement of 7,612 logic cells and 18 Block RAMS (BRAMs), and power consumption, draining only 180 mW, with comparable latency (780 $\mu$s/img) and accuracy (97%). On SHD and AudioMNIST, Spiker+ requires 18,268 and 10,124 logic cells, respectively, requiring 51 and 16 BRAMs, consuming 430 mW and 290 mW, with an accuracy of 75% and 95%. These results underscore the significance of Spiker+ in the hardware-accelerated SNN landscape, making it an excellent solution for deploying configurable and tunable SNN architectures in resource and power-constrained edge applications.
Spiker+:一种用于边缘推理的高效Spiker神经网络FPGA加速器生成框架
在边缘的嵌入式系统中包括人工神经网络,允许应用程序直接在网络外围运行的设备中利用人工智能功能,在边缘设备的边界内包含敏感数据。这有助于实时决策,降低延迟和功耗,并增强隐私和安全性。在这些环境中,脉冲神经网络(snn)提供了一种很有前途的计算范式。然而,在资源受限的边缘设备中部署高效snn需要高度并行和可重构的硬件实现。我们介绍Spiker+,这是一个综合框架,用于在现场可编程门阵列上生成高效,低功耗和低面积SNN加速器,用于边缘推断。Spiker+提供了一个可配置的多层SNN硬件架构,一个高效神经元架构库,以及一个设计框架,可以轻松地基于python定制加速器。Spiker+在三个基准数据集上进行了测试:MNIST、Spiking Heidelberg Dataset (SHD)和AudioMNIST。在MNIST上,它在资源分配方面优于最先进的SNN加速器,需要7,612个逻辑单元和18个块ram (bram),功耗仅为180 mW,具有相当的延迟(780 $\mu$s/img)和精度(97%)。在SHD和AudioMNIST上,Spiker+分别需要18,268和10,124个逻辑单元,分别需要51和16个bram,消耗430兆瓦和290兆瓦,精度为75%和95%。这些结果强调了Spiker+在硬件加速SNN领域的重要性,使其成为在资源和功率受限的边缘应用中部署可配置和可调SNN架构的绝佳解决方案。
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
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
×
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