Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning

In this work, the thermal characteristics and steady-state temperatures (SST) of CPU and FPGA of electronic system in nuclear power plant are explored. Finite element analysis is performed to simulate the test process. Furthermore, three machine learning algorithms are used to predict chips temperatures at different operating conditions. It is found that when the ambient temperature is 20 °C and all the fans are power-off, the SST of the CPU and FPGA reaches 75 °C and 72 °C, respectively. While when the fans are power-on, the SST of the CPU and FPGA drops to 37.5 °C and 33 °C. When the ambient temperature increases to 55 °C and all the fans are power-on, the SST of the CPU and FPGA is 72.3 °C and 68.2 °C, respectively. The finite element model is verified and used to generate test data. Three machine learning models are verified by predicting the SST of CPU and FPGA under different operating conditions. It is found that M-SVR has better prediction ability than DT and ANN. The findings can be used for chip reliability evaluation of other electronic system devices, and provide a new method for predicting the possible steady-state temperature of chips under different service conditions.