LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

The paper introduces Unconventional Hardware Neural Architecture Search (UH-NAS), a hardware-agnostic framework utilizing LLMs as evolutionary operators for optimizing neural network architectures across different platforms. UH-NAS facilitates the co-optimization of task accuracy and inference energy by integrating platform-specific energy models and simulators, demonstrating superior performance in discovering robust architectures for optical MZI hardware compared to traditional NAS methods. This approach is significant for practitioners as it enables effective cross-platform comparisons and enhances the design of neural networks tailored for unconventional hardware environments.