arXiv Machine Learning June 25, 2026 By Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo, Antonio Frisoli, Claudio Loconsole

On-Device Neural Architecture Search

Read the original on arXiv Machine Learning →

arXiv:2606. 24900v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop

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By Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo, Antonio Frisoli, Rodolfo Zunino

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A Low-Cost Hybrid Reservoir Computing Model for Isolated Sign Language Video Recognition

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Running hardware-aware neural architecture search on embedded devices under 512MB of RAM

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