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