Stochastic Iterative Approximation: Software/hardware techniques for adjusting aggressiveness of approximation

Abstract

Approximate computing (AC) reduces power consumption and increases execution speed in exchange for computational accuracy. By adjusting the accuracy of approximation at runtime to reflect the optimal quality of the application, which changes constantly depending on the user’s cognitive ability and attention, AC achieves even higher efficiency. In this paper, we propose stochastic iterative approximation (SIA) that achieves dynamic and rapid control of the aggressiveness of the approximation. SIA executes a single binary code with multiple level of approximate aggressiveness that are dynamically adjusted. We propose a software implementation of SIA and hardware techniques to further improve the performance of SIA. We implement a compiler and a processor simulator for SIA as the dynamic approximation modules of RISC-V and evaluate their performance. Simulation results on six benchmarks show an adjustable trade-off between output quality and execution efficiency depending on the aggressiveness of the approximation in a single binary run.