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.

中文翻译：

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选择合适的边缘设备：在GPGPU上实现基于CUDA的CNN的功耗和性能评估

机器学习（ML）作为一种强大的技术的出现一直在帮助几乎所有业务领域提高运营效率或发展新的价值主张。除了部署和维护ML模型的挑战之外，选择合适的边缘设备（例如GPGPU）来运行这些模型（例如具有大量计算过程的CNN）也是当今组织面临的最紧迫的挑战之一。由于租用（在云上）或购买边缘设备的成本与最终产品或服务的成本直接相关，因此选择最高效的设备至关重要。但是，此决策需要深入了解在边缘设备上运行的ML模型的性能和功耗，这些知识必须在ML工作流的早期阶段进行识别。在本文中，我们提出了一种基于ML的新颖方法，可为ML工程师提供GPGPU上基于CUDA的CNN的功耗和性能的早期估计。所提出的方法使ML工程师能够在开发的早期阶段为给定的CNN模型选择最高效的GPGPU。