We explore the application of robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs and heterogenous systems that combine GPUs and CPUs. A 64-bit Tegra TX1 SoC is configured with DVFS enabled and multiple CUDA benchmarks are used to train and test models optimized for each frequency and voltage point. These optimized models are then compared with a simpler unified model that uses a single set of model coefficients for all frequency and voltage points of interest. To obtain this unified model, a number of experiments are conducted to extract information on idle, clock and static power to derive power usage from a single reference equation. T

Learn about power modelling on GPUs and CPUs


Jose Nunez-Yanez, Kris Nikov, Kerstin Eder, and Mohammad Hosseinabady.'Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling'In Proceedings of the PARMA-DITAM'2020. Association for Computing Machinery, New York, NY, USA, Article 2, 1–6,2020.
Jose Nunez-Yanez, Geza Lore, ',Enabling accurate modeling of power and energy consumption in an ARM-based System-on-Chip,Microprocessors and Microsystems,Volume 37, Issue 3,2013,
Nikov, Krastin & Nunez-Yanez, Jose & Horsnell, Matthew.'Evaluation of Hybrid Run-Time Power Models for the ARM Big.LITTLE Architecture. 2015.

Check out the projects in github


Power_Modeling_ARM_A57
CPU_Power_Modelling
GPU_tx1