ApproxMA: Approximate Memory Access for Dynamic Precision Scaling
Invited conference paper presented and published in conference proceedings

Other information
AbstractMotivated by the inherent error-resilience of emerging recognition, mining, and synthesis (RMS) applications, approximate computing techniques such as precision scaling has been advocated for achieving energy-efficiency gains at the cost of small accuracy loss. Most existing solutions, however, focus on the approximation of on-chip computations without considering that of off-chip data accesses, whose energy consumption may contribute to a significant portion of the total energy. In this work, we propose a novel approximate memory access technique for dynamic precision scaling, namely ApproxMA. To be specific, by taking both runtime data precision constraints and error-resilient capabilities of the application into consideration, ApproxMA determines the precision of data accesses and loads scaled data from off-chip memory for computation. Experimental results with mixture model-based clustering algorithms demonstrate the efficacy of the proposed methodology.
All Author(s) ListYe Tian, Qian Zhang, Ting Wang, Feng Yuan, Qiang Xu
Name of ConferenceGLSVLSI 2015
Start Date of Conference20/05/2015
End Date of Conference22/05/2015
Place of ConferencePittsburgh, Pennsylvania
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 25th edition on Great Lakes Symposium on VLSI
Pages337 - 342
LanguagesEnglish-United States

Last updated on 2018-22-01 at 05:56