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Smart hardware architecture with random weight elimination and weight balancing algorithms
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)
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Submitter: 浅井 哲也
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