文献
J-GLOBAL ID:202102276266453330
整理番号:21A0150849
浮動小数点演算増幅器を越えて:クリティカルデータパス長によるCNN性能予測【JST・京大機械翻訳】
Beyond Floating-Point Ops: CNN Performance Prediction with Critical Datapath Length
著者 (4件):
Langerman David
(University of Pittsburgh, NSF Center for Space, High-performance, and Resilient Computing (SHREC),Department of Electrical and Computer Engineering,Pittsburgh,PA,USA)
,
Johnson Alex
(University of Pittsburgh, NSF Center for Space, High-performance, and Resilient Computing (SHREC),Department of Electrical and Computer Engineering,Pittsburgh,PA,USA)
,
Buettner Kyle
(University of Pittsburgh, NSF Center for Space, High-performance, and Resilient Computing (SHREC),Department of Electrical and Computer Engineering,Pittsburgh,PA,USA)
,
George Alan D.
(University of Pittsburgh, NSF Center for Space, High-performance, and Resilient Computing (SHREC),Department of Electrical and Computer Engineering,Pittsburgh,PA,USA)
資料名:
IEEE Conference Proceedings
(IEEE Conference Proceedings)
巻:
2020
号:
HPEC
ページ:
1-9
発行年:
2020年
JST資料番号:
W2441A
資料種別:
会議録 (C)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)