A physics-informed residual neural network trained on per-unit power labels cuts power estimation error by 74 to 85 percent over the unsupervised ABPI baseline on Jetson hardware and a simulated heterogeneous SoC.
Emulation of an asic power, temperature and aging monitor system for fpga prototyping,
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CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs
A physics-informed residual neural network trained on per-unit power labels cuts power estimation error by 74 to 85 percent over the unsupervised ABPI baseline on Jetson hardware and a simulated heterogeneous SoC.