Adaptive Machine Learning-Based Temperature Prediction Scheme for Thermal-Aware NoC System

Because of the high-complex interconnection in the contemporary manycore system, the Network-on-Chip (NoC) technology is proven as an efficient way to solve the communication problem in multicore systems. However, the thermal problem becomes the main design challenge in the current NoC systems due to the high-diverse workload distribution and large power density. Therefore, the Proactive Dynamic Thermal Management (PDTM) is employed as an efficient way to control the system temperature in modern multicore systems. Based on the predicted temperature information, the PDTM can control the system temperature in advance, which helps to reduce the performance impact during the temperature control period. However, the conventional temperature prediction model is usually built based on several specific physical parameters, which are usually temperature-sensitive as well. As a result, the current temperature prediction models still suffer from large prediction errors, which reduces the benefit of the PDTM. To solve this problem, we combine the artificial neural network and LMS adaptive filter theory to propose an adaptive machine learning-based temperature prediction model. Because the proposed model can adapt to the hyperplane of the temperature behavior of NoC system during the runtime, the proposed approach can reduce average error by 37.2% to 62.3%, which helps to improve the system performance by 9.16% to 38.37% and can bring smaller area overhead than the related works by 18.59% to 22.11%.