Temperature tracking and management with number-limited thermal sensors for thermal-aware NoC systems

As the complexity of multicore system grows with respect to the technology development, the Network-on-Chip (NoC) provides flexible and scalable interconnection for the multicore systems. However, as the complexity of the network increases, the large workload diversity and the time-varying workload distribution result in large power density, which leads to severer thermal problems and makes the temperature distribution of the system become time-varying. To prevent the multicore systems from overheating, in a practical way, many thermal sensors are embedded in the system. However, due to the manufacturing cost constraints, it is not a viable option to involve a massive number of embedded thermal sensors. Therefore, searching for the appropriate locations in offline design phase to allocate the number-limited thermal sensors, which will be used to sense the time-varying system temperature behavior at runtime, is a design challenge. On the other hand, full-chip temperature distribution tracking based on the restricted temperature sensing information affects the efficiency of the involved temperature management. In this paper, we first present a novel thermal sensor allocation methodology by considering the time-varying temperature behavior on the chip according to different applications. Besides, a linear-regression-based reconstruction algorithm is proposed to estimate the full-chip temperature distribution according to the number-limited thermal sensing results. At last, a framework of temperature management with restricted temperature sensing information is introduced. Compared with the conventional methods, the proposed approach can reduce 28% to 92% average error of full-chip temperature estimation, which helps to improve the average system throughput by 60% to 70%.