An Affordable Hardware-Aware Neural Architecture Search for Deploying Convolutional Neural Networks on Ultra-Low-Power Computing Platforms

Hardware-aware neural architecture search (HW-NAS) allows the integration of convolutional neural networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints. However, state-of-the-art HW-NAS target high-performance microc...

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Bibliographic Details
Published in:IEEE sensors letters 2024-05, Vol.8 (5), p.1-4
Main Authors: Garavagno, Andrea Mattia, Ragusa, Edoardo, Frisoli, Antonio, Gastaldo, Paolo
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer architecture
Computer vision
Costs
Embedded systems
Hardware
hardware-aware neural architecture search (HW-NAS)
Kernel
Microcontrollers
Power consumption
Power management
Random access memory
Search problems
Searching
Sensor applications
Sensors
TinyML
Training
ultra-low-power computing
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Summary:Hardware-aware neural architecture search (HW-NAS) allows the integration of convolutional neural networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints. However, state-of-the-art HW-NAS target high-performance microcontrollers, whose power consumption does not meet sensing nodes requirements. This letter presents a HW-NAS generating tiny CNNs that can run on ultra-low-power microcontrollers, featuring a lightweight search procedure enabling its execution even on embedded devices. Empirical results on three well-known benchmarks for tiny computer vision proved that the proposed HW-NAS was able to generate tiny CNNs while preserving state-of-the-art classification accuracy.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2024.3387056