Work-in-Progress: Early Power Estimation of CUDA-based CNNs on GPGPUs

The increasing application of Machine Learning (ML) techniques in the Internet of Things (IoT) devices has led designers to leverage ML accelerators like GPGPUs in such devices. However, choosing the most appropriate accelerator for such IoT devices is very challenging as they commonly should adhere to tight constraints e.g., low power consumption, long battery lifetime, and low cost of the final products. As a consequence, designing such application-specific IoT devices becomes a non-trivial and difficult task. In this paper, we present a novel approach to estimate power consumption of CUDA-based Convolutional Neural Networks (CNNs) on GPGPUs in the design phase. Our approach is able to provide designers with an early prediction of CNNs power consumption up to an absolute error of less than 2% in comparison to the real hardware execution.