Adaptive Deep Reinforcement Learning Optimization Design Process for Hybrid Pin-Fin Microchannel Heat Sink Based on Hybrid Neural Network Acceleration

With the growing complexity of integrated circuits and heterogeneous microsystem integration, effective thermal management and intelligent design are crucial for preventing thermal failures. This study proposes an adaptive deep reinforcement learning-based (ADRL) optimization method for pin-fin microchannel heat sinks, enhancing iterative optimization efficiency with a hybrid neural network (HNN). A hybrid pin-fin microchannel model is established, with simulation-generated datasets capture temperature and pressure drop performance under various parameters. The proposed HNN significantly improves the prediction accuracy under small sample conditions. Compared with deep neural network (DNN) and convolutional neural network (CNN), the root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and comprehensive standard deviation (SD) are reduced by 92.6%, 93.7%, 92.5%, 97.2% and 96.4%, 93.7%, 90%, 92.3% respectively, effectively mapping structural parameters to performance indicators. Using the proximal policy optimization (PPO) algorithm in the ADRL framework, this method optimizes the hybrid heat sink layout under non-uniform heat flow conditions, reducing the maximum temperature of high-power heat sources by 5.34%, the maximum temperature of low-power heat sources by 10.01%, and the pressure drop by 53.05%.