All Stochastic-Spiking Neural Network (AS-SNN): Noise Induced Spike Pulse Generator for Input and Output Neurons With Resistive Synaptic Array

  • Kim, Honggu; 
  • An, Yerim; 
  • Kim, Minchul; 
  • Heo, Gyeong-Chan; 
  • Shim, Yong
Citations

WEB OF SCIENCE

9
Citations

SCOPUS

1

초록

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.

키워드

Neurons; Noise; Codes; Calibration; Voltage; Stochastic processes; Hardware; Operational amplifiers; Synapses; Resistance; Spiking neural network; neuromorphic system; stochastic neuron
제목
All Stochastic-Spiking Neural Network (AS-SNN): Noise Induced Spike Pulse Generator for Input and Output Neurons With Resistive Synaptic Array
저자
Kim, Honggu; An, Yerim; Kim, Minchul; Heo, Gyeong-Chan; Shim, Yong
DOI
10.1109/TCSII.2024.3485178
발행일
2025-01
유형
Article
저널명
IEEE Transactions on Circuits and Systems II: Express Briefs
권
72
호
1
페이지
78 ~ 82