Neural architecture codesign for fast physics applications
We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. Th...
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| Автори: | , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
IOP Publishing
2025-01-01
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| Серія: | Machine Learning: Science and Technology |
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1088/2632-2153/adede1 |
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