Variation-aware statistical energy optimization on voltage-frequency island based MPSoCs under performance yield constraints

Energy efficiency is a primary design concern for embedded multiprocessor system-on-chips (MPSoCs). Recently, Voltage-Frequency Island (VFI) - based design paradigm was introduced for fine-grained power management, which can seamlessly combine with the task scheduling algorithm to optimize system energy. However, the ever-increasing variabilities cause large uncertainty on delay and power. Such statistical nature in performance parameters easily makes deterministic energy optimization hard to achieve desirable performance yield, defined as the probability of the design meeting timing constraints of the system. In this paper, we propose a variation-aware statistical energy optimization framework, which takes account of performance yield constraints in energy-aware task scheduling, voltage assignment and VFI partitioning process. Energy optimization sensitivity, defined as energy variations of the task under voltage scaling, combines with the statistical slack of the task to guide the overall optimization flow. Experimental results demonstrate the effectiveness of the proposed scheme.