Edge-AI deployment is bottlenecked by data-movement energy; pairing event-driven vision sensors with in-memory analog compute could lift that ceiling by orders of magnitude. Both technologies are individually mature; the framework distinguishing fabricated demonstrations from projected systems is missing. Of six application domains surveyed (robotics, autonomous vehicles, AR/VR, surveillance, medical imaging, IoT), half rest entirely on projection, and existing hardware sits at Technology Readiness Levels 2-5. This evidence-graded review applies a three-paradigm architectural taxonomy and benchmarks the gap against current digital neuromorphic alternatives. It identifies an end-to-end integrated DVS-memristor system as the field's open challenge, with testable accuracy and power targets.

翻译：边缘人工智能的部署受限于数据移动能耗；将事件驱动视觉传感器与存内模拟计算相结合，可将能效提升数个数量级。两项技术各自已趋于成熟，但当前缺乏区分实际制造原型与理论所推演系统的评估框架。在所调研的六大应用领域（机器人、自动驾驶、增强现实/虚拟现实、安防监控、医学成像、物联网）中，半数完全依赖理论推演，现有硬件技术成熟度仅处于2-5级。本项基于证据等级的综述采用三范式架构分类法，将技术差距与当前数字神经形态替代方案进行基准对比，指出端到端集成的动态视觉传感器-忆阻器系统是该领域的开放性挑战，并提出了可验证的精度与功耗目标。