Digital implementation of a multilayer perception based on stochastic computing with learning function

Sasaki, Yoshiaki; Muramatsu, Seiya; Nishida, Kohei et al.

Nonlinear theory and its applications, IEICE, 2022, 13(2), 324-329

Permalink : https://hdl.handle.net/2115/85558

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アクセス権
open access
URI
https://hdl.handle.net/2115/85558
タイトル
Digital implementation of a multilayer perception based on stochastic computing with learning function
著者
Sasaki, Yoshiaki ; Muramatsu, Seiya ; Nishida, Kohei ; Akai-Kasaya, Megumi ; Asai, Tetsuya
著者名
Sasaki, Yoshiaki
著者名
Muramatsu, Seiya
著者名
Nishida, Kohei
e-Rad_Researcher
50437373
著者名
Akai-Kasaya, Megumi
e-Rad_Researcher
00312380
著者名
Asai, Tetsuya
言語
英語
キーワード
stochastic computing
machine learning
neural networks
edge AI integrated circuit
発行日
2022
出版者
IEICE - Institute of the Electronics, Information and Communication Engineers
収録物名
Nonlinear theory and its applications, IEICE
巻(号)
13(2)
ページ
324-329
抄録
Stochastic Computing (SC) [2] is a probability-based computing method, which enables the performance of various operations with a small number of logic gates (i.e., low power) in exchange for high accuracy. Using SC for edge artificial intelligence (AI) integrated circuits can help circumvent the limitations inherent in the power and area required for edge AI. In this study, a three-layered Neural Network (NN) is presented with an online learning function that introduces pseudo-activation, pseudo-subtraction, and imperfect addition into the SC framework. This method may expand the options for edge AI integrated circuits using SC.
資源タイプ
学術雑誌論文
出版タイプ
Version of Record
権利情報
Copyright ©2022 The Institute of Electronics, Information and Communication Engineers
PISSN
2185-4106
関連情報 (isIdenticalTo)
出版社版DOI
https://doi.org/10.1587/nolta.13.324