Resistive CAM Acceleration for Tunable Approximate Computing

The Internet of Things significantly increases the amount of data generated, straining the processing capability of current computing systems. Approximate computing is a promising solution to accelerate computation by trading off energy and accuracy. In this paper, we propose a resistive content addressable memory (CAM) accelerator, called RCA, which exploits data locality to have an approximate memory-based computation. RCA stores high frequency patterns and performs computation inside CAM without using processing cores. During execution time, RCA searches an input operand among all prestored values on a CAM and returns the row with the nearest distance. To manage accuracy, we use a distance metric which considers the impact of each bit indices on computation accuracy. We evaluate an application of proposed RCA on CPU approximation, where RCA can be used as a stand-alone or as a hybrid computing unit besides CPU cores for tunable CPU approximation. We evaluate the architecture of the proposed RCA using HSPICE and multi2sim by testing our results on x86 CPU processor. Our evaluation shows that RCA can accelerate CPU computation by 12.6× and improve the energy efficiency by 6.6× as compared to a traditional CPU architecture, while providing acceptable quality of service.