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