A Nearest-Neighbor-Based Thermal Sensor Allocation and Temperature Reconstruction Method for 3-D NoC-Based Multicore Systems

To avoid overheating of 3-D network-on-chip (NoC)-based multicore systems, many researchers have used dynamic thermal management (DTM) techniques, which need embedded thermal sensors to provide accurate temperature information. However, only a few sensors can be embedded due to the limited hardware cost. So, it is crucial to find an appropriate way to allocate number-limited sensors at design time and reconstruct full-chip temperature accurately using the limited temperature information. However, the relationship between the non-sensor-allocated nodes and the sensor-allocated nodes modeled by the existing methods has a deviation from the actuality, which leads to an inaccurate temperature reconstruction. Another problemis that the existing methods depend highly on the training data. The estimation error can be significant when the running application's traffic characteristic differs from the one in the offline phase. This article presents a sensor allocation method based on the cores' spatial correlation, which is not dependent on the training data. Our allocation method contains two stages: 1) using our nearest-neighbor-based initialization algorithm to allocate sensors preliminarily and 2) using genetic algorithm (GA) to optimize the initial allocation. Besides, we use artificial neural network (ANN) to reconstruct the full-chip temperature. Compared with the state-of-the-art methods, our method can improve the average accuracy of the estimated temperature under different scenarios by 17.60%-88.63%. What is more, our approach has high flexibility and can adapt to different application scenarios with high accuracy with only one offline training.