WattLayer: Get Layers Right to Estimate Inference Energy of Neural Networks
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Computer Science > Machine Learning
Title:WattLayer: Get Layers Right to Estimate Inference Energy of Neural Networks
Abstract:The widespread adoption of Artificial Intelligence (AI) has led to increasing concerns about energy consumption, yet there is a lack of standardized methodologies to accurately estimate AI inference energy consumption, particularly across various tasks and architectures. In this study, we propose a task independent, layer-wise energy estimation model for AI architectures. Our model is evaluated on a large dataset of more than 100,000 layers for 295 neural network architectures across 3 widely-used tasks and 3 distinct hardware platforms. Our approach achieves a median error of 19.6%, outperforming state-of-the-art methods. We further show that layer-wise decomposition generalize to new tasks without complete retraining, by leveraging shared layers across architectures. It offer tools, insights and a precise methodology to empower stakeholders in designing energy-efficient AI systems.
| Comments: | Accepted at IJCAI-ECAI 2026 Workshop SuRE |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.27841 [cs.LG] |
| (or arXiv:2606.27841v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27841
arXiv-issued DOI via DataCite (pending registration)
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