全随机尖峰神经网络(AS-SNN):电阻式突触阵列输入输出神经元的噪声诱导尖峰脉冲发生器

IF 4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Honggu Kim;Yerim An;Minchul Kim;Gyeong-Chan Heo;Yong Shim
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引用次数: 0

摘要

基于峰值神经网络(SNN)的混合信号神经形态硬件由于其高能效的数据处理特性,与传统计算平台相比,在速度和能效方面具有很高的优势。然而,在芯片上实现泊松尖峰序列来表示尖峰编码的数据还没有完全实现。此外,混合信号神经形态硬件中的模拟电路元件容易发生变化,这可能导致SNN应用中的精度下降。在这篇简短的文章中,我们展示了用于在片上实现泊松尖峰串的鲁棒噪声诱导尖峰脉冲发生器。在我们的工作中开发的随机s型神经元比LIF神经元对各种RRAM器件变化因素具有更好的鲁棒性:1)随机电报噪声(RTN), 2)故障卡滞(SAFs)和3)持久故障,保证了SNN的鲁棒性应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
All Stochastic-Spiking Neural Network (AS-SNN): Noise Induced Spike Pulse Generator for Input and Output Neurons With Resistive Synaptic Array
Spiking neural network (SNN) based mixed-signal neuromorphic hardware gives high benefit in terms of speed and energy efficiency compared to conventional computing platform, thanks to its energy efficient data processing nature. However, on-chip realization of Poisson spike train to represent spike-encoded data has not yet fully achieved. Furthermore, the analog circuit components in mixed-signal neuromorphic hardwares are prone to variations which might lead to accuracy drop in SNN applications. In this brief, we demonstrated robust noise induced spike pulse generator for on-chip realization of Poisson spike train. The stochastic sigmoid neuron developed in our work exhibits better robustness than LIF neurons towards diverse RRAM device variation factors: 1) Random Telegraph Noise (RTN), 2) Stuck-At-Faults (SAFs) and 3) Endurance failures, guaranteeing robust SNN application.
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来源期刊
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Circuits and Systems II: Express Briefs 工程技术-工程:电子与电气
CiteScore
7.90
自引率
20.50%
发文量
883
审稿时长
3.0 months
期刊介绍: TCAS II publishes brief papers in the field specified by the theory, analysis, design, and practical implementations of circuits, and the application of circuit techniques to systems and to signal processing. Included is the whole spectrum from basic scientific theory to industrial applications. The field of interest covered includes: Circuits: Analog, Digital and Mixed Signal Circuits and Systems Nonlinear Circuits and Systems, Integrated Sensors, MEMS and Systems on Chip, Nanoscale Circuits and Systems, Optoelectronic Circuits and Systems, Power Electronics and Systems Software for Analog-and-Logic Circuits and Systems Control aspects of Circuits and Systems.
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