Program Acceleration Using Nearest Distance Associative Search

Data generated by current computing systems is rapidly increasing as they become more interconnected as part of the Internet of Things (IoT). The growing amount of generated data, such as multimedia, needs to be accelerated using efficient massive parallel processors. Associative memories, in tandem with processing elements, in the form of look-up tables, can reduce energy consumption by eliminating redundant computations. In this paper, we propose a resistive associative unit, called RAU, which approximately performs basic computations with significantly higher efficiency compared to traditional processing units. RAU stores high frequency patterns corresponding to each operation and then retrieves the nearest distance row to the input data as an approximate output. In order to avoid using a large and energy intensive RAU, our design adaptively detects inputs with lower frequency and assigns them to precise cores to process. For each application, our design is able to adjust the ratio of data processed between RAU and precise cores to ensure computational accuracy. We consider the application of RAU on an AMD Southern Island GPU, a recent GPGPU architecture. Our experimental evaluation shows that GPGPU enhanced with RAU can achieve 61% average energy savings, and 2.2x speedup over eight diverse OpenCL applications, while ensuring acceptable quality of computation. The energy-delay product improvement of enhanced GPGPU is 5.7x and 2.8x higher compared to conventional and state-of-the-art approximate GPGPU, respectively.