Execution time prediction for energy-efficient hardware accelerators

Tao Chen, Alexander Rucker, G. Edward Suh

Abstract: Many mobile applications utilize hardware accelerators for computation-intensive tasks. Often these tasks involve real-time user interactions and must finish within a certain amount of time for smooth user experience. In this paper, we propose a DVFS framework for hardware accelerators involving real-time user interactions. The framework automatically generates a predictor for each accelerator that predicts its execution time, and sets a DVFS level to just meet the response time requirement. Our evaluation results show, compared to running each accelerator at a constant frequency, our DVFS framework achieves 36.7% energy savings on average across a set of accelerators, while only missing 0.4% of the deadlines. The energy savings are only 3.8% less than an optimal DVFS scheme. We show with the introduction of a boost level, the deadline misses can be completely eliminated while still achieving 36.4% energy savings.