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Smart hardware architecture with random weight elimination and weight balancing algorithms

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/85561

Title: Smart hardware architecture with random weight elimination and weight balancing algorithms
Authors: Ali, Emiliano J. Browse this author
Amemiya, Yoshiki Browse this author
Akai-Kasaya, Megumi Browse this author →KAKEN DB
Asai, Tetsuya Browse this author →KAKEN DB
Keywords: hardware neural networks
random weight elimination
unsupervised learning
smart architecture
weight balancing
Issue Date: 2022
Publisher: IEICE - Institute of the Electronics, Information and Communication Engineers
Journal Title: Nonlinear theory and its applications, IEICE
Volume: 13
Issue: 2
Start Page: 336
End Page: 342
Publisher DOI: 10.1587/nolta.13.336
Abstract: Reducing the number of connections in hardware artificial neural networks, as compared with their software counterparts, can result in a drastic reduction in costs, because the reduction translates into utilizing fewer devices. This paper presents the demonstration of a method, by using simulations, to halve the amount of weights in a network while minimizing the accuracy loss. Additionally, the appropriate considerations for translating these simulation results to hardware networks are also detailed.
Rights: Copyright ©2022 The Institute of Electronics, Information and Communication Engineers
Type: article
URI: http://hdl.handle.net/2115/85561
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 浅井 哲也

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