IEEE - Institute of Electrical and Electronics Engineers, Inc. - WRA: A 2.2-to-6.3 TOPS Highly Unified Dynamically Reconfigurable Accelerator Using a Novel Winograd Decomposition Algorithm for Convolutional Neural Networks

Author(s): Chen Yang ; Yizhou Wang ; Xiaoli Wang ; Li Geng
Sponsor(s): IEEE Circuits and Systems Society
Publisher: IEEE - Institute of Electrical and Electronics Engineers, Inc.
Publication Date: 1 September 2019
Volume: 66
Page(s): 3,480 - 3,493
ISSN (Electronic): 1558-0806
ISSN (Paper): 1549-8328
DOI: 10.1109/TCSI.2019.2928682
Regular:

As convolutional neural networks (CNNs) become more and more diverse and complicated, acceleration of CNNs increasingly encounters a bottleneck of balancing performance, energy efficiency, and... View More

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