Exploring an Efficient Approach for Architecture-Level Thermal Simulation of Multi-core CPUs

Lin, Jenn-Sen; Dowling, Anthony; Liu, Yu; Cheng, Ming‐C.

doi:10.1109/iscas48785.2022.9937274

Cited by 1 publication

(3 citation statements)

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“…6(a Accuracy of the POD model in ( 5) is dictated by the quality of the coefficients evaluated in ( 6)- (10). The thermal conductance elements g i,j in the POD space results from the projection of the heat flux (−k∇T ) onto the POD modes and thus strongly depends on gradients of the modes, as defined in (7) and (9). The accuracy of the POD model is thus significantly influenced by the gradients of the generated POD modes that are directly impacted by the quality of the training data.…”

Section: B Demonstrations Of the Pod Simulation Methodologymentioning

confidence: 99%

“…Additionally, it is difficult to apply the Green's function method to 3D dynamic thermal simulations. In recent years, the data-driven approach based on proper orthogonal decomposition (POD), together with the guidance of physical principles, has become increasingly attractive for thermal simulation of semiconductor chips due to its ability to achieve high accuracy, resolution and efficiency simultaneously [7]- [9]. These thermal simulation approaches are further discussed in Sec.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, an architecture-level thermal simulator has been developed based on the POD-Galerkin (PODTherm-GP) methodology for 3D dynamic thermal simulation of a multi-core CPU. The early concept of this methodology was briefly illustrated in [7]. This POD-Galerkin modeling technique offers an accurate and efficient prediction of the thermal profile in the multi-core processor without a priori assumptions.…”

Section: Introductionmentioning

confidence: 99%

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PODTherm-GP: A Physics-Based Data-Driven Approach for Effective Architecture-Level Thermal Simulation of Multi-Core CPUs

Lin

Dowling

Cheng

et al. 2023

IEEE Trans. Comput.

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A thermal simulation methodology derived from the proper orthogonal decomposition (POD) and the Galerkin projection (GP), hereafter referred to as PODTherm-GP, is evaluated in terms of its efficiency and accuracy in a multi-core CPU. The GP projects the heat transfer equation onto a mathematical space whose basis functions are generated from thermal data enabled by the POD learning algorithm. The thermal solution data are collected from FEniCS using the finite element method (FEM) accounting for appropriate parametric variations. The GP incorporates physical principles of heat transfer in the methodology to reach high accuracy and efficiency. The dynamic power map for the CPU in FEM thermal simulation is generated from gem5 and McPACT, together with the SPLASH-2 benchmarks as the simulation workload. It is shown that PODTherm-GP offers an accurate thermal prediction of the CPU with a resolution as fine as the FEM. It is also demonstrated that PODTherm-GP is capable of predicting the dynamic thermal profile of the chip with a good accuracy beyond the training conditions. Additionally, the approach offers a reduction in degrees of freedom by more than 5 orders of magnitude and a speedup of 4 orders, compared to the FEM.

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Section: B Demonstrations Of the Pod Simulation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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