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Holmes : A Hardware-Oriented Optimizer Using Logarithms

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/87632

Title: Holmes : A Hardware-Oriented Optimizer Using Logarithms
Authors: Yamagishi, Yoshiharu Browse this author
Kaneko, Tatsuya Browse this author
Akai-Kasaya, Megumi Browse this author →KAKEN DB
Asai, Tetsuya Browse this author →KAKEN DB
Keywords: optimizer
edge computing
neural network
nonvolatile mem-ory
quantization
Issue Date: Dec-2022
Publisher: IEICE - Institute of the Electronics, Information and Communication Engineers
Journal Title: IEICE transactions on information and systems
Volume: E105D
Issue: 12
Start Page: 2040
End Page: 2047
Publisher DOI: 10.1587/transinf.2022PAP0001
Abstract: Edge computing, which has been gaining attention in re-cent years, has many advantages, such as reducing the load on the cloud, not being affected by the communication environment, and providing excellent security. Therefore, many researchers have attempted to implement neural networks, which are representative of machine learning in edge computing. Neural networks can be divided into inference and learning parts; however, there has been little research on implementing the learning component in edge computing in contrast to the inference part. This is because learning requires more memory and computation than inference, easily exceeding the limit of resources available for edge computing. To overcome this prob-lem, this research focuses on the optimizer, which is the heart of learning. In this paper, we introduce our new optimizer, hardware-oriented logarith-mic momentum estimation (Holmes), which incorporates new perspectives not found in existing optimizers in terms of characteristics and strengths of hardware. The performance of Holmes was evaluated by comparing it with other optimizers with respect to learning progress and convergence speed. Important aspects of hardware implementation, such as memory and oper-ation requirements are also discussed. The results show that Holmes is a good match for edge computing with relatively low resource requirements and fast learning convergence. Holmes will help create an era in which advanced machine learning can be realized on edge computing.
Rights: Copyright ©2022 The Institute of Electronics, Information and Communication Engineers
https://search.ieice.org/
Type: article (author version)
URI: http://hdl.handle.net/2115/87632
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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