Accuracy-Aware Power Management for Many-Core Systems Running Error-Resilient Applications

Abstract

Power capping techniques based on dynamic voltage and frequency scaling (DVFS) and power gating (PG) are oriented toward power actuation, compromising on performance and energy. Inherent error resilience of emerging application domains, such as Internet-of-Things (IoT) and machine learning, provides opportunities for energy and performance gains. Leveraging accuracy-performance tradeoffs in such applications, we propose approximation (APPX) as another knob for close-looped power management, to complement power knobs with performance and energy gains. We design a power management framework, APPEND+, that can switch between accurate and approximate modes of execution subject to system throughput requirements. APPEND+ considers the sensitivity of the application to error to make disciplined alteration between levels of APPX such that performance is maximized while error is minimized. We implement a power management scheme that uses APPX, DVFS, and PG knobs hierarchically. We evaluated our proposed approach over machine learning and signal processing applications along with two case studies on IoT—early warning score system and fall detection. APPEND+ yields $1.9\times $ higher throughput, improved latency up to five times, better performance per energy, and dark silicon mitigation compared with the state-of-the-art power management techniques over a set of applications ranging from high to no error resilience.