Machine Learning-Assisted Thermoelectric Cooling for On-Demand Multi-Hotspot Thermal Management

arXiv:2404.13441v1 Announce Type: cross
Abstract: The rapid emergence of System-on-Chip (SoC) technology introduces multiple dynamic hotspots with spatial and temporal evolution to the system, necessitating a more efficient, sophisticated, and intelligent approach to achieve on-demand thermal management. In this study, we present a novel machine learning-assisted optimization algorithm for thermoelectric coolers (TECs) that can achieve global optimal temperature by individually controlling TEC units based on real-time multi-hotspot conditions across the entire domain. A convolutional neural network (CNN) with inception module …

abstract algorithm arxiv chip cooling cs.lg demand dynamic emergence evolution hotspots intelligent machine machine learning management multiple novel optimization physics.app-ph soc spatial study system-on-chip technology temporal type