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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Science China. Information sciences Ročník 67; číslo 2; s. 122405
Hlavní autori:	Wu, Weiwei, Tu, Fengbin, Li, Xiangyu, Wei, Shaojun, Yin, Shouyi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Beijing Science China Press 01.02.2024 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!