Presentation 2022-12-16
Evaluation of Fault Tolerance in Stochastic Computing Based Neural Network Inference Accelerators
Ryoga Nagashima, Stefan Holst, Xiaoqing Wen,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) In recent years, neural networks have become increasingly complex. Stochastic computing (SC) techniques are currently being explored to address the increased circuit area and power consumption. Besides area and power benefits of SC, fault tolerance is often mentioned as a big advantage over classic binary computing. However, the effects of faults in SC have yet to be fully investigated. In this study, we focus on the fault tolerance of stochastic computing and implement the processing element (PE) of a neural network using a conventional binary circuit and a stochastic computing circuit, and conduct a comparative evaluation of the fault-tolerant performance.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Stochastic / Neural Network / Fault
Paper # DC2022-74
Date of Issue 2022-12-09 (DC)

Conference Information
Committee DC
Conference Date 2022/12/16(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English) Safety, etc.
Chair Tatsuhiro Tsuchiya(Osaka Univ.)
Vice Chair Toshinori Hosokawa(Nihon Univ.)
Secretary Toshinori Hosokawa(Nihon Univ.)
Assistant

Paper Information
Registration To Technical Committee on Dependable Computing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Evaluation of Fault Tolerance in Stochastic Computing Based Neural Network Inference Accelerators
Sub Title (in English)
Keyword(1) Stochastic
Keyword(2) Neural Network
Keyword(3) Fault
1st Author's Name Ryoga Nagashima
1st Author's Affiliation Kyushu Institute of Technology(Kyutech)
2nd Author's Name Stefan Holst
2nd Author's Affiliation Kyushu Institute of Technology(Kyutech)
3rd Author's Name Xiaoqing Wen
3rd Author's Affiliation Kyushu Institute of Technology(Kyutech)
Date 2022-12-16
Paper # DC2022-74
Volume (vol) vol.122
Number (no) DC-318
Page pp.pp.12-16(DC),
#Pages 5
Date of Issue 2022-12-09 (DC)