An Approximate Computing Stack Based on Computation Reuse

Approximate computing has been studied widely in computing systems ranging from hardware to software. Approximate computing is a paradigm for reducing execution time and power consumption by tolerating some quality loss in computed results. On the other hand, we have proposed a processor called auto-memoization processor which is based on computation reuse. The processor dynamically detects functions as reusable blocks, and automatically stores their inputs and outputs into a lookup table. Then, when the processor detects the same block, the processor compares the current input sequence with past input sequences stored in the table. If the current input sequence matches one of the input sequences in the lookup table, the processor writes back the associated outputs, and skips the execution of the function. Here, by tolerating partial input mismatch in computation reuse, approximate computing can be achieved. In this paper, we propose an approximate computing stack based on computation reuse. The stack includes a programming framework which allows programmers to easily apply approximate computing to various applications, a compiler, and the modified auto-memoization processor. Through an evaluation with cjpeg from MediaBench, by tolerating partial input mismatch in computation reuse, execution cycles are reduced by 22.3% in maximum, and reuse rate is improved by 29.5% in maximum with negligible quality deterioration in outputs.