  Thermal analysis provides deeper insights into electronic chips behavior
under different temperature scenarios and enables faster design exploration.
However, obtaining detailed and accurate thermal profile on chip is very
time-consuming using FEM or CFD. Therefore, there is an urgent need for
speeding up the on-chip thermal solution to address various system scenarios.
In this paper, we propose a thermal machine-learning (ML) solver to speed-up
thermal simulations of chips. The thermal ML-Solver is an extension of the
recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning
Simulator) with modifications to the solution algorithm to handle constant and
distributed HTC. The proposed method is validated against commercial solvers,
such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different
scenarios to demonstrate its enhanced accuracy, scalability, and
generalizability.
