USING COMPUTING FIRST PRINCIPLES TO IMPROVE THE SYMBIOTIC PERFORMANCE IN ALGORITHMS AND PROCESSORS USED IN LOW-POWERED MACHINE LEARNING

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USING COMPUTING FIRST PRINCIPLES TO IMPROVE THE SYMBIOTIC PERFORMANCE IN ALGORITHMS AND PROCESSORS USED IN LOW-POWERED MACHINE LEARNING

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Summary, etc.：: Using less electric power or speeding up processing is catching the interests of researchers in deep learning. Models have grown in complexity and siz...

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Material Type: 博士論文
Title: USING COMPUTING FIRST PRINCIPLES TO IMPROVE THE SYMBIOTIC PERFORMANCE IN ALGORITHMS AND PROCESSORS USED IN LOW-POWERED MACHINE LEARNING
Author/Editor: ンシンガ, ロバート
Author Heading: ンシンガ, ロバートンシンガ, ロバート
Publication Date: 2022-09-20
Publication Date (W3CDTF): 2022-09-20
Alternative Title: コンピューティングの第一原理を使用して、低電力の機械学習で使用されるアルゴリズムとプロセッサの共生パフォーマンスを向上させる
Dissertation Number: 甲先第436号
Text Language Code: und
Subject Heading: embedded system
IEEE754-2008
floating point
digital signal processor
Q format notation
Target Audience: 一般
Format (IMT): application/pdf
Source: fulltext
https://tokushima-u.repo.nii.ac.jp/record/2010412/files/k3652_fulltext.pdf
Access Restrictions: インターネット公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 徳島大学 : 徳島大学機関リポジトリ