An ensemble data-learning approach based on proper orthogonal decomposition (POD) and Galerkin projection
(EnPOD-GP) is proposed for thermal simulations of multi-core CPUs to improve training efficiency and the
model accuracy for a previously developed global POD-GP method (GPOD-GP). GPOD-GP generates one set of
basis functions (or POD modes) to account for thermal behavior in response to variations in dynamic power
maps (PMs) in the entire chip, which is computationally intensive to cover possible variations of all power
sources. EnPOD-GP however acquires multiple sets of POD modes to significantly improve training efficiency
and effectiveness, and its simulation accuracy is independent of any dynamic PM. Compared to finite element
simulation, both GPOD-GP and EnPOD-GP offer a computational speedup over 3 orders of magnitude. For a
processor with a small number of cores, GPOD-GP provides a more efficient approach. When high accuracy
is desired and/or a processor with more cores is involved, EnPOD-GP is more preferable in terms of training
effort and simulation accuracy and efficiency. Additionally, the error resulting from EnPOD-GP can be precisely
predicted for any random spatiotemporal power excitation.
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Fast Accurate Full-Chip Dynamic Thermal Simulation with Fine Resolution Enabled by a Learning Method
The need for full-chip dynamic thermal simulation for effective runtime thermal management of multicore processors
has been growing in recent years due to the rising demand for high-performance computing. In addition to simulation efficiency and accuracy, a high resolution is desirable in order to accurately predict crucial hot spots in the chip. This work investigates a simulation technique derived from proper orthogonal decomposition (POD) for full-chip dynamic thermal simulation of a multicore processor. The POD projects a heat transfer problem onto a mathematical space constituted by a finite set of basis functions (or POD modes) that are generated (or trained) by thermal solution data collected from direct numerical simulation (DNS). Accuracy and efficiency of the POD simulation technique influenced by the quality of thermal data are examined thoroughly, especially in the areas with high thermal gradients. The results show that if the POD modes are trained by good-quality data, the POD simulation offers an accurate prediction of the dynamic thermal distribution in the multicore processor with an extremely small degree of freedom (DoF). A reduction in computational time over four orders of magnitude, compared to the DNS, can be achieved for full-chip dynamic thermal simulation with a resolution as fine as the DNS. The study has also demonstrated that the POD approach can be used to rigorously verify the accuracy of solutions offered by DNS tools. A practical approach is proposed to further enhance the accuracy and efficiency of the proposed full-chip thermal simulation technique.
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- Award ID(s):
- 2003307
- NSF-PAR ID:
- 10420997
- Publisher / Repository:
- https://ieeexplore.ieee.org/document/9987551
- Date Published:
- Journal Name:
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- ISSN:
- 0278-0070
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCAD.2022.3229598
