CASA: correlation-aware speculative adders

Abstract

Speculative adders divide addition into subgroups and execute them in parallel for higher execution speed and energy efficiency, but at the risk of generating incorrect results. In this paper, we propose a lightweight correlation-aware speculative addition (CASA) method, which exploits the correlation between input data and carry-in values observed in real-life benchmarks to improve the accuracy of speculative adders. Experimental results show that applying the CASA method leads to a significant reduction in error rate with only marginal overhead in timing, area, and power consumption.

DOI: 10.1145/2627369.2627635

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Cite this paper

@inproceedings{Liu2014CASACS, title={CASA: correlation-aware speculative adders}, author={Gai Liu and Ye Tao and Mingxing Tan and Zhiru Zhang}, booktitle={ISLPED}, year={2014} }