  Thermoelectric coolers (TECs) offer a promising solution for direct cooling
of local hotspots and active thermal management in advanced electronic systems.
However, TECs present significant trade-offs among spatial cooling, heating and
power consumption. The optimization of TECs requires extensive simulations,
which are impractical for managing actual systems with multiple hotspots under
spatial and temporal variations. In this study, we present a novel machine
learning-assisted optimization algorithm for thermoelectric coolers that can
achieve global optimal temperature by individually controlling TEC units based
on real-time multi-hotspot conditions across the entire domain. We train a
convolutional neural network (CNN) with a combination of the Inception module
and multi-task learning (MTL) approach to comprehend the coupled
thermal-electrical physics underlying the system and attain accurate
predictions for both temperature and power consumption with and without TECs.
Due to the intricate interaction among passive thermal gradient, Peltier effect
and Joule effect, a local optimal TEC control experiences spatial temperature
trade-off which may not lead to a global optimal solution. To address this
issue, we develop a backtracking-based optimization algorithm using the machine
learning model to iterate all possible TEC assignments for attaining global
optimal solutions. For any m by n matrix with NHS hotspots (n, m <= 10, 0<= NHS
<= 20), our algorithm is capable of providing 52.4% peak temperature reduction
and its corresponding TEC array control within an average of 1.64 seconds while
iterating through tens of temperature predictions behind-the-scenes. This
represents a speed increase of over three orders of magnitude compared to
traditional FEM strategies which take approximately 27 minutes.
