Energy-Aware Deep Learning on Resource-Constrained Hardware

• URL: http://arxiv.org/abs/2505.12523v1
• Date: Sun, 18 May 2025 19:17:03 GMT
• Title: Energy-Aware Deep Learning on Resource-Constrained Hardware
• Abstract summary: Deep learning (DL) on Internet of Things (IoT) and mobile devices offers numerous advantages over cloud-based processing.<n>Such devices face substantial energy constraints to prolong battery-life, or may even operate intermittently via energy-harvesting.<n>We present an overview of such approaches, outlining their methodologies, implications for energy consumption and system-level efficiency.
• Score: 6.02520094856825
• License: http://creativecommons.org/licenses/by/4.0/
• 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, \textit{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.

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