Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural architecture search (NAS) methods are typically tailored to a single hardware family, limiting cross-platform comparison and generalization. We introduce Unconventional Hardware Neural Architecture Search (UH-NAS), a hardware-agnostic, LLM-guided NAS framework that integrates language models as evolutionary operators to co-optimize accuracy and inference energy. By exposing hardware as a swappable backend with per-platform energy models, physical constraints, and non-ideality simulators, UH-NAS enables fair system-level comparisons across various backends without modifying the search algorithm. Tested on optical MZI hardware, UH-NAS discovers more diverse, robust architectures than conventional baselines while outperforming existing LLM-to-NAS approaches. Additional ablations on architecture robustness under non-idealities and the role of system prompts highlight the importance of architecture-hardware co-design for emerging computing platforms.

翻译：在非常规硬件上部署神经网络需要同时优化任务精度与平台特定约束（如能量成本、物理非理想性和数值精度）的架构。现有的神经网络架构搜索（NAS）方法通常针对单一硬件系列定制，限制了跨平台比较和泛化能力。我们提出非常规硬件神经网络架构搜索（UH-NAS），这是一种硬件无关、LLM引导的NAS框架，将语言模型作为进化算子集成，以协同优化精度和推理能量。通过将硬件暴露为可替换的后端（包含每种平台的能量模型、物理约束和非理想性模拟器），UH-NAS无需修改搜索算法即可实现跨多个后端的公平系统级比较。在光学MZI硬件上测试时，UH-NAS比传统基准发现了更多样化、更鲁棒的架构，同时优于现有的LLM-to-NAS方法。关于架构在非理想性下的鲁棒性以及系统提示作用的额外消融研究，突显了新兴计算平台中架构-硬件协同设计的重要性。