MASC: Ultra-Low Energy Multiple-Access Single-Charge TCAM for Approximate Computing

Abstract

Memory-based computing using associative memory has emerged as a promising solution to reduce the energy consumption of important classes of streaming applications such as multimedia by avoiding redundant computations. In associative memory, a set of frequent patterns that represent basic functions are pre-stored in ternary content addressable memory (TCAM) and reused. The primary limitation to using associative memory in modern parallel processors is the large search energy required by TCAMs. In TCAMs, all match rows, except hit rows, precharge and discharge in every search operation, resulting in high and undesirable energy consumption. In this paper, we propose a new multiple-access single-charge (MASC) TCAM architecture which is capable of searching TCAM contents multiple times with a single precharging cycle. In contrast to previous designs, the MASC TCAM keeps the match-line voltage of all miss-rows high and uses their charge for the next search operation, while only the hit rows discharge. We use a periodic refresh scheme to guarantee the accuracy of the search. We also implement a new type of approximate associative memory by setting longer refresh times for MASC TCAMs, which yields search results within 1–2 bit Hamming distances of the exact result. Our evaluation on AMD Southern Island GPU shows that using MASC associative memory can improve the average GPGPU energy efficiency by 36.6%, 40.2% and 39.4% for exact matching, selective 1-HD and 2-HD approximations respectively, with acceptable quality of service (PSNR>30dB). These energy savings are 1.8X and 1.6X higher than GPGPU using exact matching TCAM and approximation TCAM that uses voltage overscaling, respectively.