Computer Science > Computer Vision and Pattern Recognition
[Submitted on 30 Aug 2021 (v1), last revised 12 Oct 2021 (this version, v2)]
Title:Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
View PDFAbstract:The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of global warming and climate change. With the progress of efficient deep learning techniques, e.g., model compression, researchers can obtain efficient models with fewer parameters and smaller latency. However, most of the existing efficient deep learning methods do not explicitly consider energy consumption as a key performance indicator. Furthermore, existing methods mostly focus on the inference costs of the resulting efficient models, but neglect the notable energy consumption throughout the entire life cycle of the algorithm. In this paper, we present the first large-scale energy consumption benchmark for efficient computer vision models, where a new metric is proposed to explicitly evaluate the full-cycle energy consumption under different model usage intensity. The benchmark can provide insights for low carbon emission when selecting efficient deep learning algorithms in different model usage scenarios.
Submission history
From: Bo Li [view email][v1] Mon, 30 Aug 2021 18:22:36 UTC (3,104 KB)
[v2] Tue, 12 Oct 2021 02:23:42 UTC (3,355 KB)
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