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CLC number: TP302.1

On-line Access: 2020-10-14

Received: 2020-02-26

Revision Accepted: 2020-05-13

Crosschecked: 2020-09-29

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Jing Wang


Wei-gong Zhang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.10 P.1426-1441


Multi-dimensional optimization for approximate near-threshold computing

Author(s):  Jing Wang, Wei-wei Liang, Yue-hua Niu, Lan Gao, Wei-gong Zhang

Affiliation(s):  College of Information Engineering, Capital Normal University, Beijing 100056, China; more

Corresponding email(s):   jwang@cnu.edu.cn, zwg771@cnu.edu.cn

Key Words:  Approximate computing, Near-threshold computing, Output quality predictor, Energy, Performance

Jing Wang, Wei-wei Liang, Yue-hua Niu, Lan Gao, Wei-gong Zhang. Multi-dimensional optimization for approximate near-threshold computing[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1426-1441.

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A1 - Jing Wang
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000089

The demise of Dennard’s scaling has created both power and utilization wall challenges for computer systems. As transistors operating in the near-threshold region are able to obtain flexible trade-offs between power and performance, it is regarded as an alternative solution to the scaling challenge. A reduction in supply voltage will nevertheless generate significant reliability challenges, while maintaining an error-free system that generates high costs in both performance and energy consumption. The main purpose of research on computer architecture has therefore shifted from performance improvement to complex multi-objective optimization. In this paper, we propose a three-dimensional optimization approach which can effectively identify the best system configuration to establish a balance among performance, energy, and reliability. We use a dynamic programming algorithm to determine the proper voltage and approximate level based on three predictors: system performance, energy consumption, and output quality. We propose an output quality predictor which uses a hardware/software co-design fault injection platform to evaluate the impact of the error on output quality under near-threshold computing (NTC). Evaluation results demonstrate that our approach can lead to a 28% improvement in output quality with a 10% drop in overall energy efficiency; this translates to an approximately 20% average improvement in accuracy, power, and performance.





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