Energy-Efficient Design of Processing Element for Convolutional Neural Network

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Convolutional neural network (CNN) is the most prominent algorithm for its wide usage and good performance. Despite the fact that the processing element (PE) plays an important role in CNN processing, there has been no study focusing on PE design optimized for state-of-the-art CNN algorithms. In this brief, we propose an optimal PE implementation including a data representation scheme, circuit block configurations, and control signals for energy-efficient CNN. To validate the excellence of this brief, we compared our proposed design with several previous methods, and fabricated a silicon chip. The software simulation results demonstrated that we can reduce 54% of data bit lengths with negligible accuracy loss. Our optimization on PE achieves to save computing power up to 47%, and an accelerator exploiting our method shows superior results in terms of power, area, and external DRAM access.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-11
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, v.64, no.11, pp.1332 - 1336

ISSN
1549-7747
DOI
10.1109/TCSII.2017.2691771
URI
http://hdl.handle.net/10203/227505
Appears in Collection
EE-Journal Papers(저널논문)
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