Q-learning Assisted LASSO-based Thermal Sensor Placement for Thermal-aware Multi-core Systems

Thermal problems become severe in contemporary multi-core systems because of the complicated workload and high power density. The problems impact the system performance and damage the system reliability. The practical way to monitor the system temperature is to place number-limited thermal sensors on multi-core systems. Unfortunately, finding proper locations for thermal sensor placement is an NP-hard problem. Many pieces of research proposed methods to allocate number-limited thermal sensors under different perspectives. However, the conventional methods still do not consider the time-varying temperature distribution or the correlation between different thermal hotspot points. To solve these problems, we apply the Q-learning method to assist with thermal sensor placements determined by the Least Absolute Shrinkage and Selection Operator (LASSO) theory. While LASSO primarily excels in feature selection, Q-learning is better equipped to handle dynamic environmental changes and interdependencies. Thus, we employ the Q-learning method to formulate a precise cost function for accurately estimating the outcomes following the placement of a thermal sensor at a specific location. Compared with the state-of-the-art, our proposed methods, using both the LASSO-based method and the Q-learning Assisted LASSO method, reduce the average errors by 67%-85% and 69%-87%, respectively, and the maximum errors by 80%-92% and 82%-93%.