SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training

Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, \textit{SparseTrain} is proposed to accelerate CNN training by fully exploiting the sparsity. It mainly involves three levels of innovations: activation gradients pruning algorith...

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Veröffentlicht in:	arXiv.org 2020-07
Hauptverfasser:	Dai, Pengcheng, Yang, Jianlei, Ye, Xucheng, Cheng, Xingzhou, Luo, Junyu, Song, Linghao, Chen, Yiran, Zhao, Weisheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Architecture Artificial neural networks Back propagation Back propagation networks Computer simulation Convolution Energy conversion efficiency Neural networks Performance evaluation Sparsity Topology Training
Online-Zugang:	Volltext
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