On-Device Neural Architecture Search
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Computer Science > Machine Learning
Title:On-Device Neural Architecture Search
Abstract:This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors. This new adaptation capability can be particularly useful in the case of human-machine interfaces for which the neural network analyzing the biometrical data can be re-designed each time the user changes, after a guided data collection procedure, fighting the typical data variations between individuals on a new level. To implement the proposed approach a new NAS has been designed and then validated on the Italian Sign Language dataset (ISL), a collection of surface electromyography (sEMG) signals of the signs of the Italian alphabet, using several embedded systems. Moreover, further validation on the Case Western Reserve University dataset (CWRU), a benchmark for intelligent fault diagnosis, is presented to suggest another possible application of the proposed approach. When run on a Raspberry Pi 4, the proposed NAS performs beyond the state of the art proposing a tiny neural architecture having 0.63 times less RAM occupancy and 5.96 percentage points of more accuracy in the case of the ISL dataset; and 0.44 times less RAM occupancy and 0.2 percentage points of more accuracy in the case of the CWRU dataset.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.24900 [cs.LG] |
| (or arXiv:2606.24900v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24900
arXiv-issued DOI via DataCite
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| Related DOI: | https://doi.org/10.1109/ICCE63647.2025.10929801
DOI(s) linking to related resources
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Submission history
From: Andrea Mattia Garavagno [view email][v1] Fri, 12 Jun 2026 13:40:31 UTC (112 KB)
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