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1 Ergebnisse
1
η-LSTM : co-designing highly-efficient large LSTM traini..:
, In:
Proceedings of the 48th Annual International Symposium on Computer Architecture
,
Zhang, Xingyao
;
Xia, Haojun
;
Zhuang, Donglin
... - p. 567-580 , 2021
Link:
https://dl.acm.org/doi/10.1109/ISCA52012.2021.00051
RT T1 <cTitle>Proceedings of the 48th Annual International Symposium on Computer Architecture</cTitle>: T1 <Title>η-LSTM : co-designing highly-efficient large LSTM training via exploiting memory-saving and architectural design opportunities</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3502666&Exemplar=1&LAN=DE A1 Zhang, Xingyao A1 Xia, Haojun A1 Zhuang, Donglin A1 Sun, Hao A1 Fu, Xin A1 Taylor, Michael B. A1 Song, Shuaiwen Leon PB IEEE Press YR 2021 K1 accelerator K1 machine learning K1 neural nets K1 recurrent neural network K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 567 OP 580 LK http://dx.doi.org/https://dl.acm.org/doi/10.1109/ISCA52012.2021.00051 DO https://dl.acm.org/doi/10.1109/ISCA52012.2021.00051 SF ELIB - SuUB Bremen
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