Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918099894140928 |
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| author | Shi, Yujia Njor, Emil Martínez-Nuevo, Pablo Shepstone, Sven Ewan Fafoutis, Xenofon |
| author_facet | Shi, Yujia Njor, Emil Martínez-Nuevo, Pablo Shepstone, Sven Ewan Fafoutis, Xenofon |
| contents | The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexity of designing TinyML systems hampers their broad adoption. To reduce this complexity, we introduce "Data Aware Differentiable Neural Architecture Search". Unlike conventional Differentiable Neural Architecture Search, our approach expands the search space to include data configuration parameters alongside architectural choices. This enables Data Aware Differentiable Neural Architecture Search to co-optimize model architecture and input data characteristics, effectively balancing resource usage and system performance for TinyML applications. Initial results on keyword spotting demonstrate that this novel approach to TinyML system design can generate lean but highly accurate systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications Shi, Yujia Njor, Emil Martínez-Nuevo, Pablo Shepstone, Sven Ewan Fafoutis, Xenofon Machine Learning The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexity of designing TinyML systems hampers their broad adoption. To reduce this complexity, we introduce "Data Aware Differentiable Neural Architecture Search". Unlike conventional Differentiable Neural Architecture Search, our approach expands the search space to include data configuration parameters alongside architectural choices. This enables Data Aware Differentiable Neural Architecture Search to co-optimize model architecture and input data characteristics, effectively balancing resource usage and system performance for TinyML applications. Initial results on keyword spotting demonstrate that this novel approach to TinyML system design can generate lean but highly accurate systems. |
| title | Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.15545 |