Neural Architecture Search (NAS) has emerged as a powerful framework for automatically discovering neural architectures that balance accuracy and efficiency. However, as AI transitions from static benchmarks to real-world deployment, the traditional focus on hardware-aware efficiency is no longer sufficient. We observe that modern NAS methods, especially those that target edge AI, are evolving to address a triple objective: Efficiency, Robustness, and Continual Learning. While efficiency ensures feasibility in resource-constrained environments, robustness guarantees reliability under environmental variabilities, and continual learning enables adaptation to sequential tasks without catastrophic forgetting. We propose a taxonomy of NAS approaches through this triple lens, distinguishing between methods targeting resource optimization, environmental resilience, and architectural plasticity. This unified perspective reveals that these axes, though often studied in isolation, are mutually reinforcing. Building on this taxonomy, we map the current landscape of these NAS methods into a new framework called Hardware-Efficient, Robust, and ContinUal LEarning Search (HERCULES). We define the desiderata, the twelve labours of HERCULES, addressing the non-trivial challenge of balancing an adequate search-space exploration with the immense computational costs of a multi-objective NAS, accounting for these crucial objectives of current AI systems. By identifying critical gaps in existing research, this survey outlines a roadmap toward integrated algorithmic, architectural, and hardware-software co-design for truly deployable, lifelong-learning AI systems.

翻译：神经架构搜索（NAS）已成为自动发现兼顾精度与效率的神经架构的强大框架。然而，随着人工智能从静态基准测试转向实际部署，传统上对硬件感知效率的关注已不再足够。我们观察到，现代NAS方法，尤其是针对边缘AI的方法，正朝着解决三重目标演进：效率、鲁棒性和持续学习。其中，效率确保资源受限环境下的可行性，鲁棒性保障环境变化时的可靠性，而持续学习则使模型能够在顺序任务中适应而不会发生灾难性遗忘。我们通过这三重视角提出NAS方法的分类法，区分了针对资源优化、环境适应性和架构可塑性的不同方法。这一统一视角揭示了这些维度虽然常被独立研究，但实为相互增强。基于这一分类法，我们绘制了当前NAS方法的全貌，并将其整合至一个名为“硬件高效、鲁棒且持续学习搜索（HERCULES）”的新框架中。我们定义了HERCULES的十二项需求，以应对在充分探索搜索空间与多目标NAS巨大计算成本之间取得平衡的严峻挑战，同时兼顾当前AI系统的这些关键目标。通过识别现有研究中的关键空白，本综述勾勒出一条迈向集成算法、架构及软硬件协同设计的路线图，旨在构建真正可部署的终身学习AI系统。