Thermal considerations are a critical facet in SoC and System design. There are numerous difficulties in performing comprehensive thermal analysis on modern SoC designs as well as considerable difficulty in moving towards a cross-discipline co-design strategy. The design space is large and growing more complex with each generation, coupled with long evaluation/simulation time for sufficiently accurate thermal response. Thermal feedback into design iterations were additionally slowed by the huge numbers of excitation (workloads) scenarios needed to provide design robustness. Augmented Intelligence and machine learning (ML) approaches are explored to address some of these difficulties, as well as development of a fast evaluation function to reduce total computation time. Various clustering and modeling techniques are used to improve stimulus/workload selection and coverage for analysis, which further reduces evaluation time. This huge enhancement in evaluation time has opened new opportunities for co-design work, ML optimization schemes are applied to address the high degrees of freedom present at the SoC level. The results have been impressive, showing huge potential for thermal improvements which translate directly into improved product performance.

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