Extending SpArSe: Automatic Gesture Recognition Architectures for Embedded Devices
| Autor: | Ernesto Biempica, Juan Borrego-Carazo, Jordi Carrabina, David Castells-Rufas |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Contextual image classification Computer science business.industry Bayesian optimization 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network Memory management Recurrent neural network Gesture recognition Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | ICMLA |
| DOI: | 10.1109/icmla51294.2020.00011 |
| Popis: | Neural Architecture Search (NAS), which allows for automatically developing neural networks, has been mostly devoted to performance on a single metric, usually accuracy. New approaches have added more objectives, such as model size, in order to find networks suitable for resource-constrained platforms. SpArSe [1] is a multi-objective Bayesian optimization framework for automatically developing image classification convolutional neural networks (CNNs) for micro-controller units (MCUs). In this work, we first implement SpArSe and modify it to reduce search time, obtaining similar results regarding accuracy, model size, and maximum working memory but in less optimization time. Moreover, we extend the search space to include recurrent neural networks (RNNs) and add an inference latency objective for time-constrained tasks. Finally, we test our implementation in a gesture recognition task obtaining better results than previous manually tuned approaches for size and performance metrics, which validates the approach and its utility. |
| Databáze: | OpenAIRE |
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