S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computing system design. Through optimizing parallel execu...

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Veröffentlicht in:	arXiv.org 2021-06
Hauptverfasser:	Yang, Jianlei, Fu, Wenzhi, Cheng, Xingzhou, Ye, Xucheng, Dai, Pengcheng, Zhao, Weisheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Cognitive tasks Computation Computer architecture Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Hardware Architecture Computer Science - Learning Convolution Data transmission Design optimization Neural networks Sparsity Systems design
Online-Zugang:	Volltext
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