New preprint survey on energy-aware deep learning on embedded hardware
Josh Millar has just released the latest survey paper he lead on energy-aware approaches to optimise deep-learning training and inference on embedded devices, such as those benchmarked in "Benchmarking Ultra-Low-Power µNPUs" recently.
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.
Any comments, please do let any of us know!
Energy-Aware Deep Learning on Resource-Constrained Hardware
Josh Millar, Hamed Haddadi and Anil Madhavapeddy.
Working paper at arXiv.
papers