Time-Frame Integrate-and-Fire Neuron Circuit for Low Energy Inference Hardware Spiking Neural Networks

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yeonwoo Kim;Bosung Jeon;Jonghyuk Park;Woo Young Choi
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Abstract

Hardware spiking neural networks (SNNs) are gaining increasing attention as promising next-generation computing approaches owing to their parallel computing and energy-efficient nature. Recent SNNs have achieved excellent performance by introducing artificial neural networks (ANN) to the SNN conversion training method. However, SNNs face significant challenges when implemented on hardware platforms. These challenges include achieving low energy consumption, difficulties in minimizing conversion error, and representing ANN functions other than the rectified linear unit activation function. This paper proposes mixed-signal complementary metal-oxide-semiconductor time-frame integrate-and-fire (TIF) neuron circuits and corresponding SNNs to achieve high accuracy with low energy consumption. TIF neurons integrate synaptic inputs during a time-frame and generate output spikes under synchronization signals based on the integrated input. When building SNNs with TIF neurons, bias and max-pooling can be effectively implemented, which is difficult to achieve using conventional SNNs with integrate-and-fire (IF) neuron circuits. In addition, TIF neurons support pipelining operations, further enhancing the inference throughput of the system. Simulation results with the CIFAR-10 dataset show that SNNs with TIF neurons achieve 0.48 %p higher classification accuracy while consuming 92 % lower neuron energy than conventional IF-based SNNs.
用于低能耗推理硬件尖峰神经网络的时帧集成与发射神经元电路
硬件尖峰神经网络(SNN)因其并行计算和高能效的特性,作为有前途的下一代计算方法正受到越来越多的关注。最近,通过将人工神经网络(ANN)引入 SNN 转换训练方法,SNN 取得了卓越的性能。然而,在硬件平台上实现 SNN 时面临着重大挑战。这些挑战包括实现低能耗、转换误差最小化的困难,以及表示除整流线性单元激活函数以外的 ANN 函数。本文提出了混合信号互补金属氧化物半导体时帧积分发射(TIF)神经元电路和相应的 SNN,以实现高精度和低能耗。TIF 神经元在一个时间框架内整合突触输入,并根据整合输入在同步信号下产生输出尖峰。使用 TIF 神经元构建 SNN 时,可以有效实现偏置和最大池化,而使用传统的集成-发射(IF)神经元电路 SNN 很难实现这一点。此外,TIF 神经元还支持流水线操作,进一步提高了系统的推理吞吐量。CIFAR-10 数据集的仿真结果表明,与传统的基于 IF 的 SNN 相比,采用 TIF 神经元的 SNN 的分类准确率提高了 0.48%p,而神经元能耗却降低了 92%。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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