Designing deep networks that meet strict latency and accuracy constraints on edge accelerators increasingly relies on hardware-aware optimization, including neural architecture search (NAS) guided by device-level metrics. Yet most hardware-aware NAS pipelines still optimize architectures under full-precision assumptions and apply low-precision adaptation only after the search, leading to a mismatch between optimization-time behavior and deployment-time execution on low-precision hardware that can substantially degrade accuracy. We address this limitation by integrating deployment-aligned low-precision training directly into hardware-aware NAS. Candidate architectures are exposed to FP16 numerical constraints during fine-tuning and evaluation, enabling joint optimization of architectural efficiency and numerical robustness without modifying the search space or evolutionary strategy. We evaluate the proposed framework on vessel segmentation for spaceborne maritime monitoring, targeting the Intel Movidius Myriad X Visual Processing Unit (VPU). While post-training precision conversion reduces on-device performance from 0.85 to 0.78 mIoU, deployment-aligned low-precision training achieves 0.826 mIoU on-device for the same architecture (95,791 parameters), recovering approximately two-thirds of deployment-induced accuracy gap without increasing model complexity. These results demonstrate that incorporating deployment-consistent numerical constraints into hardware-aware NAS substantially improves robustness and alignment between optimization and deployment for resource-constrained edge Artificial Intelligence (AI).

翻译：在边缘加速器上设计满足严格延迟和精度约束的深度网络，越来越依赖于硬件感知优化，包括基于设备级指标的神经架构搜索（NAS）。然而，大多数硬件感知NAS流程仍假设全精度条件优化架构，仅在搜索后应用低精度适配，导致优化阶段行为与低精度硬件部署执行不匹配，从而显著降低精度。我们通过将部署对齐的低精度训练直接集成到硬件感知NAS中来解决此限制。在微调和评估过程中，候选架构将暴露于FP16数值约束，从而在不修改搜索空间或进化策略的情况下实现架构效率与数值鲁棒性的联合优化。我们针对星载海事监测的血管分割任务评估所提框架，目标平台为Intel Movidius Myriad X视觉处理单元（VPU）。结果表明，后训练精度转换使设备性能从0.85 mIoU降至0.78 mIoU，而针对相同架构（95,791个参数）的部署对齐低精度训练在设备上达到0.826 mIoU，在无需增加模型复杂度的情况下恢复了约三分之二的部署精度差距。这些结果证明，将部署一致的数值约束纳入硬件感知NAS可显著提升资源受限边缘人工智能（AI）的鲁棒性及优化与部署之间的对齐度。