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1 Ergebnisse
1
η-LSTM: Co-Designing Highly-Efficient Large LSTM Training v..:
, In:
2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)
,
Zhang, Xingyao
;
Xia, Haojun
;
Zhuang, Donglin
... - p. 567-580 , 2021
Link:
https://doi.org/10.1109/ISCA52012.2021.00051
RT T1 <cTitle>2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)</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=ieee-9499761&Exemplar=1&LAN=DE A1 Zhang, Xingyao A1 Xia, Haojun A1 Zhuang, Donglin A1 Sun, Hao A1 Fu, Xin A1 Taylor, Michael B. A1 Leon Song, Shuaiwen YR 2021 SN 2575-713X K1 Training K1 Technological innovation K1 Sentiment analysis K1 Memory management K1 Graphics processing units K1 Benchmark testing K1 Data models K1 Machine Learning K1 Neural nets K1 Recurrent Neural Network K1 Accelerator SP 567 OP 580 LK http://dx.doi.org/https://doi.org/10.1109/ISCA52012.2021.00051 DO https://doi.org/10.1109/ISCA52012.2021.00051 SF ELIB - SuUB Bremen
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