针对低功耗设备的深度学习优化，提升能效与运行稳定性。
Energy-Aware Deep Learning on Resource-Constrained Hardware
- 提出面向资源受限设备的深度学习能效优化方法
- 涵盖推理与训练阶段，降低能耗并提升系统效率
- 适合物联网与移动设备开发者参考
在物联网和移动设备上使用深度学习（DL）相比云端处理具有诸多优势。然而，这些设备面临显著的能源限制，以延长电池寿命，或可能通过能量采集间歇性运行。因此，近年来对这类资源受限设备上的深度学习推理与训练进行能效优化的研究日益受到关注。本文综述了此类方法，阐述其技术路径、对能耗与系统级效率的影响，以及在支持网络类型、硬件平台和应用场景方面的局限性。我们希望本综述能清晰呈现不断演进的能效感知深度学习领域，并为未来在资源受限计算中的研究奠定基础。
原文摘要 · Abstract (English)
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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