GAAS: An Efficient Group Associated Architecture and Scheduler Module for Sparse CNN Accelerators

Convolutional neural networks (CNNs) have become powerful algorithms in various tasks. Application-specific integrated circuit (ASIC) has been widely used to accelerate CNN on mobile platforms because of its tremendous energy efficiency and performance. Meanwhile, CNNs have become much sparser with...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems Jg. 39; H. 12; S. 5170 - 5182
Hauptverfasser:	Wang, Jingyu, Yuan, Zhe, Liu, Ruoyang, Feng, Xiaoyu, Du, Li, Yang, Huazhong, Liu, Yongpan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerators Algorithms Application specific integrated circuits Artificial neural networks Collision rates Complexity theory Computer architecture Convolution Energy efficiency Gallium arsenide Group associated architecture hash collision reduction Indexes Integrated circuits Kernel load-balancing algorithm Modules Performance degradation Performance enhancement scheduler module sparse convolutional neural network (CNN) accelerator Task analysis
ISSN:	0278-0070, 1937-4151
Online-Zugang:	Volltext
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