80. APPROXIMATE COMPUTING USING CONFIGURABLE ASSOCIATIVE MEMORY

Department: Computer Science & Engineering
Faculty Advisor(s): Tajana S. Rosing

Primary Student
Name: Mohsen Imani
Email: moimani@ucsd.edu
Phone: 619-549-9084
Grad Year: 2018

Abstract
Modern computing machines are increasingly characterized by large scale parallelism in hardware (such as GPGPUs) and advent of large scale and innovative memory blocks. Parallelism enables expanded performance tradeoffs whereas memories enable reuse of computational work. To be effective, however, one needs to ensure energy efficiency with minimal reuse overheads. In this paper, we describe a resistive configurable associative memory (ReCAM) that enables selective approximation and asymmetric voltage overscaling to manage delivered efficiency. The ReCAM structure matches an input pattern with pre-stored ones by applying an approximate search on selected bit indices (bitline-configurable) or selective pre-stored patterns (row-configurable). To further reduce energy, we explore proper ReCAM sizing, various configurable search operations with low overhead voltage overscaling, and different ReCAM update policies. Experimental result on the AMD Southern Islands GPUs for eight applications shows bitline-configurable and row-configurable ReCAM achieve on average to 43.6% and 44.5% energy savings with an acceptable quality loss of 10%.

Industry Application Area(s)
Electronics/Photonics | Energy/Clean technology | Semiconductor

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