Composable thermal modeling and simulation for architecture-level thermal designs of multicore microprocessors

  • Authors:
  • Hai Wang;Sheldon X.-D. Tan;Duo Li;Ashish Gupta;Yuan Yuan

  • Affiliations:
  • University of Electronic Science and Technology of China, Sichuan China;University of California, Riverside, CA;Synopsys, CA;Intel Corporation, Chandler, AZ;University of Electronic Science and Technology of China, Sichuan China

  • Venue:
  • ACM Transactions on Design Automation of Electronic Systems (TODAES)
  • Year:
  • 2013

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Abstract

Efficient temperature estimation is vital for designing thermally efficient, lower power and robust integrated circuits in nanometer regime. Thermal simulation based on the detailed thermal structures no longer meets the demanding tasks for efficient design space exploration. The compact and composable model-based simulation provides a viable solution to this difficult problem. However, building such thermal models from detailed thermal structures was not well addressed in the past. In this article, we propose a new compact thermal modeling technique, called ThermComp, standing for thermal modeling with composable modules. ThermComp can be used for fast thermal design space exploration for multicore microprocessors. The new approach builds the composable model from detailed structures for each basic module using the finite difference method and reduces the model complexity by the sampling-based model order reduction technique. These composable models are then used to assemble different multicore architecture thermal models and realized into SPICE-like netlists. The resulting thermal models can be simulated by the general circuit simulator SPICE. ThermComp tries to preserve the accuracy of fine-grained models with the speed of coarse-grained models. Experimental results on a number of multicore microprocessor architectures show the new approach can easily build accurate thermal systems from compact composable models for fast architecture thermal analysis and optimization and is much faster than the existing HotSpot method with similar accuracy.