{"title":"All Stochastic-Spiking Neural Network (AS-SNN): Noise Induced Spike Pulse Generator for Input and Output Neurons With Resistive Synaptic Array","authors":"Honggu Kim;Yerim An;Minchul Kim;Gyeong-Chan Heo;Yong Shim","doi":"10.1109/TCSII.2024.3485178","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13101,"journal":{"name":"IEEE Transactions on Circuits and Systems II: Express Briefs","volume":"72 1","pages":"78-82"},"PeriodicalIF":4.0000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems II: Express Briefs","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10730760/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.