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. Any comments, please do let any of us know!

Authors

Publication Details

Journal
Front Matter
Published
2025-05-20
DOI
https://doi.org/10.59350/sypc8-z6x85
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

New preprint survey on energy-aware deep learning on embedded hardware

Anil Madhavapeddy
Front Matter
Industrial Vision Systems and Defect Detection
article

New preprint survey on energy-aware deep learning on embedded hardware

Anil Madhavapeddy
article en

Abstract

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. Any comments, please do let any of us know!

Front Matter
Openalex Percentile: Top 59%
Industrial Vision Systems and Defect Detection
DOIOpen Access PDFOpenAlex
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New preprint survey on energy-aware deep learning on embedded hardware — Anil Madhavapeddy · Front Matter (2025) | TGRS Research Map | TGRS