<ul><li>A technical paper titled “Energy-Aware Deep Learning on Resource-Constrained Hardware” was published by researchers at Imperial College London and University of Cambridge.</li><li>The paper discusses the utilization of deep learning on IoT and mobile devices as a more energy-efficient alternative to cloud-based processing, highlighting the importance of energy-aware approaches due to device energy constraints.</li><li>The overview in the paper outlines methodologies for optimizing DL inference and training on resource-constrained devices, focusing on energy consumption implications, system-level efficiency, and limitations in terms of network types, hardware platforms, and application scenarios.</li><li>Authors of the paper are Josh Millar, Hamed Haddadi, and Anil Madhavapeddy, and it is published on arXiv under the code 2505.12523, dated May 2025.</li></ul>

Energy-Aware DL: The Interplay Between NN Efficiency And Hardware Constraints (Imperial College London, Cambridge)

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