UH-NAS uses LLMs as evolutionary operators in a swappable-backend NAS to co-optimize neural architectures for accuracy and inference energy on physical hardware such as optical MZIs, producing more diverse and robust designs than baselines.
Analognas-bench: A nas benchmark for analog in- memory computing.arXiv preprint arXiv:2506.18495, 2025
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LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
UH-NAS uses LLMs as evolutionary operators in a swappable-backend NAS to co-optimize neural architectures for accuracy and inference energy on physical hardware such as optical MZIs, producing more diverse and robust designs than baselines.