Processing Acceleration with Resistive Memory-based Computation

Abstract

The Internet of Things significantly increases the amount of data generated that strains the processing capability of current computing systems. Approximate computing can accelerate the computation and dramatically reduce the energy consumption with controllable accuracy loss. In this paper, we propose a Resistive Associative Unit, called RAU, which approximates computation alongside processing cores. RAU exploits the data locality with associative memory. It finds a row which has the closest distance to input patterns while considering the impact of each bit index on the computation accuracy. Our evaluation shows that RAU can accelerate the GPGPU computation by 1.15x and improve the energy efficiency by 36% at only 10% accuracy loss.