Neural Network-Based Control of Forced-Convection and Thermoelectric Coolers

Thermoelectric cooling technology holds great promise as a spot-cooling solution for the VLSI circuits. Typically, it is combined with a forced convection cooler, such as a fan to achieve effective thermal management. The optimal driving current for the thermoelectric coolers and fan rotation speed play a crucial role in minimizing cooling power consumption while ensuring safe operating temperatures for the VLSI chip dies. Various software-based optimization frameworks have been proposed to calculate the near-optimal values in real time. Nonetheless, these frameworks suffer from several limitations, including slow runtime, high power consumption, costly optimization software license, and the need for a separate computer to execute the optimization software. This article presents a hardware-friendly neural network (NN)-based optimizer that can learn from the functionality of a software optimizer. In order to train the NN model, a self-supervised learning strategy is employed to generate the desired labeled data. Simulation results demonstrate that the proposed model successfully meets the thermal constraints across all the tested benchmarks, with an overall system cooling power consumption increase of only 3.6% compared to a software optimizer. Furthermore, the hardware implementation of the proposed model proves to be more than five orders of magnitude faster than the software-based optimizer, consuming a negligible amount of power while occupying a small die area.