LEMAX: learning-based energy consumption minimization in approximate computing with quality guarantee

Approximate computing aims to trade accuracy for energy efficiency. Various approximate methods have been proposed in the literature that demonstrate the effectiveness of relaxing accuracy requirements in a specific unit. This provides a basis for exploring simultaneous use of multiple approximate units to improve efficiency under guarantees on quality of results. In this paper, we explore the effect of combining multiple approximate units on the energy consumption and identify the best setting that minimizes energy consumption under a quality constraint. Our approach also enables changes in unit configurations throughout the program. To do this effectively, we need a method to examine the combined impact of multiple approximate units on the output quality, and configure individual units accordingly. To solve this problem, we propose LEMAX that uses gradient descent approach to identify the best configuration of the individual approximate units for a given program. We evaluate the efficacy of LEMAX in minimizing the energy consumption of several machine learning applications with varying size (i.e., number of operations) under different quality constraints. Our evaluation shows that the configuration provided by LEMAX for a system with multiple approximate units improves the energy consumption by on average, 97.7%, 83.12%, and 73.95% for quality loss of 5%, 2% and 0.5%, respectively, compared to configurations obtained for a system with a single approximate resource.