Document Type
Conference Paper
Journal/Book Title/Conference
2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
Publisher
Association for Computing Machinery
Publication Date
6-5-2016
Funder
National Science Foundation
Abstract
In this paper, we investigate the challenges of preserving energy-efficiency in a Near-Threshold Computing (NTC) GPU. Two key factors can significantly undermine the efficacy of GPUs at NTC: (a) elongated delays at NTC make the GPU applications severely sensitive toMulti-cycle Latency Datapaths (MLDs) within the GPU pipeline; and (b) process variation (PV) at NTC induces a substantial performance variance. To address these emerging challenges, we propose SwiftGPU - -an energyefficient GPU design paradigm at NTC. SwiftGPU dynamically adjusts the degree of parallelization, and the speed of the MLDs within each stream core of the GPU. The proposed scheme achieves an average of∼15% improvement in energy-efficiency over an ideal PV-free GPU, operating at the Super-Threshold regime. SwiftGPU incurs marginal area, wire-length and power overheads of 0.65%, 2.6% and 3.7%, respectively.
Recommended Citation
Prabal Basu, Hu Chen, Shamik Saha, Koushik Chakraborty and Sanghamitra Roy, SwiftGPU: Fostering Energy Efficiency in a Near-Threshold GPU Through Tactical Performance Boost. IEEE/ACM Design Automation Conference (DAC), pp. -6, June 2016, Austin, Texas.
Comments
© 2016 This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), http://dx.doi.org/10.1145/2897937.2898100.