A Fast Leakage-Aware Green’s-Function-Based Thermal Simulator for 3-D Chips

Sultan, Hameedah; Sarangi, Smruti R.

doi:10.1109/tvlsi.2020.3023464

Cited by 11 publications

(5 citation statements)

References 27 publications

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“…For the 6.09 ms case, use of 13 modes in Model-B reduces the LS error to 0.61% that is smaller than 0.72% for the coarser FEM simulation. As mentioned previously, the coarser mesh size used in our study is actually finer than those generally used in architecture-level thermal simulations [31], [33]. Even in the extrapolation case beyond the training time, if more modes are used, Model-B is actually more accurate than the FEM simulations with mesh sizes finer than what are usually used in most studies on the chip-level thermal simulations.…”

Section: B Demonstrations Of the Pod Simulation Methodologymentioning

confidence: 79%

“…The Green's function is a spatial impulse response of the chip, and the thermal solution is constructed by superposition of the impulse responses to the point sources at different locations. It is thus inherently difficult to include boundary conditions (BCs) in thermal simulation of a finite domain [29], [30] or to use Green's function in situations where the power source is close to the edge of the chip [31]. It is also difficult to apply the conventional approach to transient thermal simulation [29], [32].…”

Section: Green's Function Methodsmentioning

confidence: 99%

“…The POD models built upon these 2 data sets with coarser and finer resolutions are referred to as POD Model-A and Model-B, respectively. It is noted that the coarser mesh used in this study is finer than those used in most of studies for chip-level thermal simulations, unless an extremely fine resolution is needed [31], [33].…”

Section: Pod Model Implementation and Validation A Training Of Pod Modesmentioning

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: 79%

Section: Green's Function Methodsmentioning

confidence: 99%

Section: Pod Model Implementation and Validation A Training Of Pod Modesmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Designing appropriate thermal management policies requires a fast and accurate thermal simulator for quick evaluation, which has resulted in various thermal simulators such as 3D-ICE [50] and HotSpot [59] to emerge. Such thermal simulators [32,50,[52][53][54]59] use floorplan and power traces as inputs and generate temperature values as output. 3D-ICE [50] develops a thermal simulator and presents a transient thermal model with microchannel cooling for liquid-cooled ICs.…”

Section: Background and Related Workmentioning

confidence: 99%

CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory Systems

Siddhu¹,

Kedia²,

Pandey³

et al. 2021

Preprint

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Processing cores and the accompanying main memory working in tandem enable the modern processors. Dissipating heat produced from computation remains a significant problem for processors. Therefore, the thermal management of processors continues to be an active subject of research. Most thermal management research takes place using simulations, given the challenges involved in measuring temperatures in real processors. Fast yet accurate interval thermal simulation toolchains remain the research tool of choice to study thermal management in processors at system-level. However, since in most existing processors, core and memory are fabricated on separate packages, with the memory having lower power densities than the cores, thermal management research in processors primarily focused on the cores. Consequently, state-of-the-art interval thermal simulation toolchains remain limited to core-only simulations.The memory bandwidth limitations associated with 2D processors lead to high-density 2.5D and 3D packaging technology. 2.5D packaging technology places cores and memory on the same package. 3D packaging technology takes it further by stacking layers of memory on the top of cores themselves. These new packagings significantly increase the power density of the processors, making them prone to overheating. Therefore, mitigating thermal issues in high-density processors (packaged with stacked memory) becomes an even more pressing problem. However, given the lack of thermal modeling for memories in existing interval thermal simulation toolchains, they are unsuitable for studying thermal management for high-density processors.To address this issue, we present CoMeT , the first integrated Core and Memory interval Thermal simulation toolchain. CoMeT comprehensively supports thermal simulation of high-and low-density processors corresponding to four different core-memory (integration) configurations -off-chip DDR memory, off-chip 3D memory, 2.5D, and 3D. CoMeT supports several novel features that facilitate overlying system research. Compared to an equivalent state-of-the-art core-only toolchain, CoMeT adds only an additional ∼5% simulation-time overhead. The source code of CoMeT has been made open for public use under the MIT license.

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“…Since both of these power components are dependent on the frequency of the core, it justifies the use of frequency in our model for the prediction of temperature. Finally, the leakage power is dependent on the temperature of the system and can be linearly modeled as described in Reference 5. In Equation (),

P_{l e a k 0}

is the leakage power at ambient temperature (

T_{a m b}

).…”

Section: Introductionmentioning

confidence: 99%

Thermal aware learning based CPU governor

Kumar

Sran

Kaur

et al. 2022

Concurrency and Computation

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The high temperature of a processor chip reduces its lifetime by many years. Also, higher temperature increases the cooling cost as well. Hence, controlling the peak temperature and maintaining high performance are the two important objectives that are in general trade-off with each other. The dynamic voltage and frequency scaling is often used to reduce the energy consumption by running the processor on lower voltage and frequencies. This eventually decreases the temperature. In this article, we propose a linear regression-based online CPU governor to predict the future temperature with high accuracy and later adjust the frequencies of the processor such that the temperature does not increase the given threshold. Doing so controls the peak temperature as well as maintains high performance. Extensive experimentation revealed that the proposed governor decreases the peak temperature by 4%-5% without observing any performance penalty and also reduces the execution cost by 3%-10% compared to the state-of-the-art.

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A Fast Leakage-Aware Green’s-Function-Based Thermal Simulator for 3-D Chips

Cited by 11 publications

References 27 publications

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

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

CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory Systems

Thermal aware learning based CPU governor

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