Machine Learning Based Thermal Evaluation for Vertically-Composed Fine-Grained 3D CMOS

Thermal management in 3D integrated circuits is a critical challenge due to their high computational density. Heat dissipation paths from top circuit layers through bottom layers to substrate are heavily constraining heat extraction. Various thermal management frameworks have been proposed to address thermal issues in different granularities. All these frameworks require a thermal evaluation stage that characterizes the thermal profile of large designs with fast runtime. In this work, we present a machine learning based thermal evaluation method that predicts all standard cell temperatures based on features extracted from circuit CAD files. We have built thermal resistance networks for 10 benchmark circuits. We performed simulations to achieve the thermal data, and trained the thermal model with the data. The model is highly accurate and can identify all over-heated cells that need to be thermally-optimized. Runtime overhead is minimal. For a 435k-cell SPARC T2 core, the runtime for predicting all cell temperatures is as small as 3.12s, which is negligible compared to the runtime of other physical design stages.