SWG: an architecture for sparse weight gradient computation

On-device training for deep neural networks (DNN) has become a trend due to various user preferences and scenarios. The DNN training process consists of three phases, feedforward (FF), backpropagation (BP), and weight gradient (WG) update. WG takes about one-third of the computation in the whole tra...

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Veröffentlicht in:	Science China. Information sciences Jg. 67; H. 2; S. 122405
Hauptverfasser:	Wu, Weiwei, Tu, Fengbin, Li, Xiangyu, Wei, Shaojun, Yin, Shouyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Beijing Science China Press 01.02.2024 Springer Nature B.V
Schlagworte:	Artificial neural networks Back propagation networks Computer architecture Computer Science Feature maps Hardware Information Systems and Communication Service Optimization Research Paper Sparsity State of the art Training Workload Workloads CNN training sparsity gradient computation architecture
ISSN:	1674-733X, 1869-1919
Online-Zugang:	Volltext
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