Thermal Sensor Placement and Thermal Reconstruction Under Gaussian and Non-Gaussian Sensor Noises for 3-D NoC

Abstract

On-chip thermal sensors are essential for temperature management in 3-D network-on-chip (NoC) systems. However, due to the physical (area and power) or economical constraints, the number of sensors is limited. Therefore, the two critical issues we face are: 1) how to figure out an efficient thermal sensor placement with the limited number of sensors and 2) how to reconstruct the entire thermal profile based on sensor observations. Another major issue for the thermal reconstruction is the sensor measurement accuracy. Thus, online accurate full-chip thermal reconstruction under Gaussian and non-Gaussian noises is another great challenge. In this paper, a greedy thermal sensor placement algorithm maximizing the rank of the observability Gramian is proposed. A good placement algorithm always relies on a specific reconstruction method. The proposed placement algorithm is designed for the state-space-based thermal model, thus the combination of the proposed placement algorithm and the Kalman filter-based reconstruction method provides a high reconstruction accuracy under Gaussian noise. For accurate temperature reconstruction under non-Gaussian noise, the Gaussian-Sum filter is applied to 3-D NoC. Compared with the Kalman filter, the Gaussian-Sum filter can reduce the root-mean-squared-error and the max error by 29.27%–35% and 33.26%–40.6%, respectively. A reusable architecture for the Kalman filter and the Gaussian-Sum filter has been proposed. Its hardware implementation details are presented in this paper. Besides, the performance and the area are evaluated as well.