A Reconfigurable Branch Predictor for Spatial Computing Architectures
Authors: 
Yanan Lu, Leibo Liu, Jian Liu, Shouyi Yin, Shaojun WeiAuthors Info & Claims
Pages 295 - 299
Abstract
Branch predictors are widely used in general purpose processors to deal with control flows. However, control flows are spatially dispersed in spatial computing architectures and traditional branch predictors are not so much effective. This paper proposes a novel branch predictor that can be configured into different prediction modes according to the control flow characteristics. Experiment results show that the proposed predictor exceeds all the traditional predictors in terms of accuracy on a coarsegrained reconfigurable array platform. It improves the prediction accuracy by 5.09% compared to a state-of-the-art technique in the cost of slight area increment.
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Index Terms
- A Reconfigurable Branch Predictor for Spatial Computing Architectures
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Published In
June 2020
383 pages
ISBN:9781450376877
DOI:10.1145/3408127
Copyright © 2020 ACM.
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- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 September 2020
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ICDSP 2020
ICDSP 2020: 2020 4th International Conference on Digital Signal Processing
June 19 - 21, 2020
Chengdu, China
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