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 development.

翻译：机械学习(ML)作为一种强有力的技术的出现,帮助了几乎所有业务领域提高业务效率或发展新的价值主张,除了部署和维护ML模型的挑战外,为运行这些模型选择右边缘装置(例如,有大规模计算过程的CNN)也是各组织目前面临的最紧迫的挑战之一,租赁(在云上)或购买边缘装置的成本与最终产品或服务的成本直接相关,选择效率最高的装置至关重要,但是,这一决策需要深入了解在ML工作流程的早期阶段必须查明的边缘装置上运行的ML模型的性能和电耗。在本文件中,我们提出了一个基于ML的新型ML方法,向ML工程师提供关于GPGPP的电力消耗和运行的CUDA的CNN的早期估算。拟议方法授权ML工程师在开发的早期阶段为特定CNN模型选择最高效的GPGPPU。