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.
Analognets: Ml-hw co-design of noise-robust tinyml models and always-on analog compute-in-memory ac- celerator.arXiv preprint arXiv:2111.06503, 2021
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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.