Multi-Stage Tunable Approximate Search in Resistive Associative Memory

General-purpose graphics processing units (GPGPUs), as programmable accelerators, improve energy efficiency by integrating a large number of relatively small cores. In this paper, we focus on improving energy efficiency of such processing core by integrating an associative memory where function responses are prestored. Associative memories can search and recall function responses for a subset of input values therefore avoiding the actual function execution on the processing core that leads to energy saving. We propose a novel low-energy Resistive Multi-stage Associative Memory (ReMAM) architecture to significantly reduce energy of a search operation by employing selective row activation and in-advance precharging techniques. ReMAM splits the search operations in a ternary content addressable memory (TCAM) to a number of shorter searches in consecutive stages. Then, it selectively activates TCAM rows at each stage based on the hits of previous stages, thus enabling energy savings. The proposed inadvance precharging technique mitigates the delay of the sequential TCAM search and limits the number of precharges to two low-cost steps. ReMAM further implements approximation on the selective TCAM blocks to reduce the search energy that relaxes the function output in a fine-grained granularity with very low impact on accuracy of the results. Its multi-stage search operation makes ReMAM applicable to many applications such as search engines, sorting, image coding, pattern recognition, query processing, and machine learning. In this work, we show an application of proposed ReMAM on AMD Southern Island GPUs. Our experimental evaluation shows that ReMAM reduces on average GPGPU energy consumption by 35 percent in the exact mode, and 58 percent in approximate mode with average relative error lower than 10 percent. These energy savings are 1.8x and 1.5x higher than state-of-the-art associative memories used in GPGPUs in exact and approximate modes.