1 citations · 1 across the 6 of their papers we have counts for
6 papers
Physics-Aware POD-Based Learning for Ab initio QEM-Galerkin Simulations of Periodic Nanostructures
Martin Veresko, Yu Liu, Daqing Hou +1
Quantum nanostructures offer crucial applications in electronics, photonics, materials, drugs, etc. For accurate design and analysis of nanostructures and materials, simulations of…
PyPOD-GP: Using PyTorch for Accelerated Chip-Level Thermal Simulation of the GPU
Neil He, Ming-Cheng Cheng, Yu Liu
The rising demand for high-performance computing (HPC) has made full-chip dynamic thermal simulation in many-core GPUs critical for optimizing performance and extending device life…
Improving TAS Adaptability with a Variable Temperature Threshold
Anthony Dowling, Ming-Cheng Cheng, Yu Liu
Thermal-Aware Scheduling (TAS) provides methods to manage the thermal dissipation of a computing chip during task execution. These methods aim to avoid issues such as accelerated a…
Predicting Accurate Hot Spots in a More Than Ten-Thousand-Core GPU with a Million-Time Speedup over FEM Enabled by a Physics-based Learning Algorithm
Lin Jian, Yu Liu, Ming-Cheng Cheng
The classical proper orthogonal decomposition (POD) with the Galerkin projection (GP) has been revised for chip-level thermal simulation of microprocessors with a large number of c…
Regulating CPU Temperature With Thermal-Aware Scheduling Using a Reduced Order Learning Thermal Model
Anthony Dowling, Lin Jiang, Ming-Cheng Cheng +1
Modern real-time systems utilize considerable amounts of power while executing computation-intensive tasks. The execution of these tasks leads to significant power dissipation and…
PODTherm-GP: A Physics-based Data-Driven Approach for Effective Architecture-Level Thermal Simulation of Multi-Core CPUs
Lin Jiang, Anthony Dowling, Ming-C. Cheng +1
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 term…