Energy-Aware Deep Learning on Resource-Constrained Hardware
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916742611075072 |
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| author | Millar, Josh Haddadi, Hamed Madhavapeddy, Anil |
| author_facet | Millar, Josh Haddadi, Hamed Madhavapeddy, Anil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12523 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy-Aware Deep Learning on Resource-Constrained Hardware Millar, Josh Haddadi, Hamed Madhavapeddy, Anil Machine Learning Hardware Architecture 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. |
| title | Energy-Aware Deep Learning on Resource-Constrained Hardware |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2505.12523 |