Thermal Field Reconstruction for VLSI Based on Sparse Dictionary Learning

Dynamic thermal management is used to handle the thermal problem of very large scale integrated circuits (VLSI), such us multicore processors. Accurate monitoring of the temperature field can insure dynamic thermal management working correctly, guarantee the chip working performance and reliability further. The temperature field reconstruction techniques based on analysis in frequency domain ignore the information in high frequency zone, which leads to thermal field recovery inaccurate. In order to improve the precision of thermal field reconstruction, a new thermal field reconstruction method based on sparse dictionary learning technology is proposed. In this method, the prior information of temperature field is sparse represented by dictionary learning, and the location assignment scheme of temperature sensor is designed to realize the reconstruction of temperature field. The experiments prove that the proposed strategy have better performance than the methods based on analysis in frequency domain.