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The emergence of Machine Learning (ML) as a powerful technique has beenhelping nearly all fields of business to increase operational efficiency or todevelop new value propositions. Besides the challenges of deploying andmaintaining ML models, picking the right edge device (e.g., GPGPUs) to runthese models (e.g., CNN with the massive computational process) is one of themost pressing challenges faced by organizations today. As the cost of renting(on Cloud) or purchasing an edge device is directly connected to the cost offinal products or services, choosing the most efficient device is essential.However, this decision making requires deep knowledge about performance andpower consumption of the ML models running on edge devices that must beidentified at the early stage of ML workflow.  In this paper, we present a novel ML-based approach that provides MLengineers with the early estimation of both power consumption and performanceof CUDA-based CNNs on GPGPUs. The proposed approach empowers ML engineers topick the most efficient GPGPU for a given CNN model at the early stage ofdevelopment.

Pick the Right Edge Device: Towards Power and Performance Estimation of CUDA-based CNNs on GPGPUs