A new technical paper titled “A Thermal Machine Learning Solver For Chip Simulation” was published by researchers at Ansys.
Abstract
“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.”
Find the open access technical paper here. Published September 2022.
Citation: Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, and Jimin Wen. 2022. A Thermal Machine Learning Solver For Chip Simulation. In Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD (MLCAD ’22). Association for Computing Machinery, New York, NY, USA, 111–117. https://doi.org/10.1145/3551901.3556484.
Related Reading
Designing For Thermal
Solutions are needed early as thermal becomes a systems issue.
Keeping IC Packages Cool
Engineers are finding ways to effectively thermally dissipate heat from complex modules.
DRAM Thermal Issues Reach Crisis Point
Increased transistor density and utilization are creating memory performance issues.
Future Challenges For Advanced Packaging
OSATs are wrestling with a slew of issues, including warpage, thermal mismatch, heterogeneous integration, and thinner lines and spaces.
Leave a Reply