Towards Neural Hardware Search: Power Estimation of CNNs for GPGPUs with Dynamic Frequency Scaling

Machine Learning (ML) algorithms are essential for emerging technologies such as autonomous driving and application-specific Internet of Things (IoT) devices. Convolutional Neural Network (CNN) is one of the major techniques used in such systems. This leads to leveraging ML accelerators like GPGPUs to meet the design constraints. However, GPGPUs have high power consumption, and selecting the most appropriate accelerator requires Design Space Exploration (DSE), which is usually time-consuming and needs high manual effort. Neural Hardware Search (NHS) is an upcoming approach to automate the DSE for Neural Networks. Therefore, automatic approaches for power, performance, and memory estimations are needed.In this paper, we present a novel approach, enabling designers to fast and accurately estimate the power consumption of CNNs inferencing on GPGPUs with Dynamic Frequency Scaling (DFS) in the early stages of the design process. The proposed approach uses static analysis for feature extraction and Random Forest Tree regression analysis for predictive model generation. Experimental results demonstrate that our approach can predict the CNNs power consumption with a Mean Absolute Percentage Error (MAPE) of 5.03% compared to the actual hardware.