| Item type |
itemtype_ver1(1) |
| 公開日 |
2022-10-19 |
| タイトル |
|
|
タイトル |
Power-aware pruning for ultrafast, energy-efficient, and accurate optical neural network design |
|
言語 |
en |
| 著者 |
Hattori, Naoki
Masuda, Yutaka
Ishihara, Tohru
Shinya, Akihiko
Notomi, Masaya
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference, July 2022, p.1285–1290. http://dx.doi.org/10.1145/3489517.3530405 |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
With the rapid progress of the integrated nanophotonics technology, the optical neural network (ONN) architecture has been widely investigated. Although the ONN inference is fast, conventional densely connected network structures consume large amounts of power in laser sources. We propose a novel ONN design method that finds an ultrafast, energy-efficient, and accurate ONN structure. The key idea is power-aware edge pruning that derives the near-optimal numbers of edges in the entire network. Optoelectronic circuit simulation demonstrates the correct functional behavior of the ONN. Furthermore, experimental evaluations using tensor-flow show the proposed methods achieved 98.28% power reduction without significant loss of accuracy. |
|
言語 |
en |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
DAC '22: 59th ACM/IEEE Design Automation Conference. July 10-14, 2022. San Francisco California |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Association for Computing Machinery |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1145/3489517.3530405 |
| 関連情報 |
|
|
関連タイプ |
isPartOf |
|
|
識別子タイプ |
ISBN |
|
|
関連識別子 |
978-1-4503-9142-9 |
| 書誌情報 |
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
p. 1285-1290,
発行日 2022-07
|
| ファイル公開日 |
|
|
日付 |
2022-10-19 |
|
日付タイプ |
Available |
DAC_Hattori_submit.pdf (725 KB)
