A new technical paper titled “Energy-Aware Deep Learning on Resource-Constrained Hardware” was published by researchers at Imperial College London and University of Cambridge.
Abstract
“The use of deep learning (DL) on Internet of Things (IoT) and mobile devices offers numerous advantages over cloud-based processing. However, such devices face substantial energy constraints to prolong battery-life, or may even operate intermittently via energy-harvesting. Consequently, energy-aware approaches for optimizing DL inference and training on such resource-constrained devices have garnered recent interest. We present an overview of such approaches, outlining their methodologies, implications for energy consumption and system-level efficiency, and their limitations in terms of supported network types, hardware platforms, and application scenarios. We hope our review offers a clear synthesis of the evolving energy-aware DL landscape and serves as a foundation for future research in energy-constrained computing.”
Find the technical paper here. May 2025.
arXiv:2505.12523. Authors: Josh Millar, Hamed Haddadi, Anil Madhavapeddy.
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