Time and space constitute fundamental dimensions of physical reality, making their integrated processing crucial for advanced vision perception systems. Current visual information processing faces dual limitations: von Neumann architecture-induced data-transfer bottlenecks and spatial-feature processing often disregard temporal dynamics, while temporal analyzers oversimplify spatial complexity. Here we propose an artificial vision hardware enabling intrinsic temporal-spatial fusion through voltage-tunable temporal differentiation with microsecond-scale resolution and photoresponse-weighted spatial compression via pixel binning. The architecture achieves millisecond-level latency from sensing to decision in autonomous driving scenarios through in-sensor spatiotemporal fusion, eliminating external computing dependencies. Experimental validation demonstrates 95 % recognition accuracy in human actions database while the operation counts required is only 1/10 of conventional convolutional processing. This work facilitates physical-level spatiotemporal fusion through the co-optimization of photodetector arrays and weighted control circuits, which could fundamentally reshape machine vision architectures with potential extensions to real-time decision systems. Current artificial vision systems suffer from high energy consumption and latency due to the von Neumann bottleneck and separated spatiotemporal processing. Wu et al. propose a vision system achieving native spatiotemporal co-processing with millisecond latency and 95% action recognition accuracy at low computational cost.
Wu et al. (Wed,) studied this question.