Performance counters based power modeling of mobile GPUs using deep learning

GPUs have recently become important computational units on mobile devices, resulting in heterogeneous devices that can run a variety of parallel processing applications. While developing and optimizing such applications, estimating power consumption is of immense importance as energy efficiency has become the key design constraint to optimize for on these platforms. In this work, we apply deep learning techniques in building a predictive model for estimating power consumption of parallel applications on a heterogeneous mobile SoC. Our model is an artificial neural network (NN) trained using CPU and GPU hardware performance counters along with measured power data. The model is trained and evaluated with data collected using a set of graphics OpenGL workloads as well as OpenCL compute benchmarks. Our evaluations show that our model can achieve accurate power estimates with a mean relative error of 4.47% with respect to real power measurements. When compared to other models, our NN model is about 3.3x better than a statistical linear regression model and 2x better than a state-of-the-art NN model.

Subjects

Deep Learning

GPU

Mobile

Modeling

Neural Network

Performance Counter

Power

DDC Class

600: Technology

Options

Performance counters based power modeling of mobile GPUs using deep learning