Reinforcement Learning for Thermal-aware Many-core Task Allocation

Abstract

To maintain reliable operation, task allocation for many-core processors must consider the heat interaction of processor cores and network-on-chip routers in performing task assignment. Our approach employs reinforcement learning, machine learning algorithm that performs task allocation based on current core and router temperatures and a prediction of which assignment will minimize maximum temperature in the future. The algorithm updates prediction models after each allocation based on feedback regarding the accuracy of previous predictions. Our new algorithm is verified via detailed many-core simulation which includes on-chip routing. Our results show that the proposed technique is fast (scheduling performed in <1 ms) and can efficiently reduce peak temperature by up to 8°C in a 49-core processor (4.3°C on average) versus a competing task allocation approach for a series of SPLASH-2 benchmarks.