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Temporal-Spatial Fusion Vision Hardware Enables Streamlined In-Sensor Computing for Dynamic Scenes
Yi Wu1,2,3, Wenjie Deng4, Ruihao Liu2
1State Key Laboratory of Materials Low-Carbon Recycling, College of Materials Science and Engineering, Beijing University of Technology, Beijing, 100124, China.
Nature Communications
|April 15, 2026
Summary
This study introduces novel artificial vision hardware for integrated temporal-spatial processing. The system achieves high accuracy and efficiency, overcoming limitations in current machine vision systems.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Hardware Engineering
Background:
- Current vision systems face limitations due to von Neumann architecture bottlenecks.
- Spatial processing often neglects temporal dynamics, and temporal processing oversimplifies spatial information.
Purpose of the Study:
- To develop artificial vision hardware enabling intrinsic temporal-spatial fusion.
- To overcome data transfer bottlenecks and improve efficiency in machine vision.
Main Methods:
- Utilized voltage-tunable temporal differentiation with microsecond resolution.
- Implemented photoresponse-weighted spatial compression via pixel binning.
- Achieved in-sensor spatiotemporal fusion for millisecond-level latency.
Main Results:
- Demonstrated 95% recognition accuracy on a human actions database.
- Reduced computational requirements to 1/10th of conventional methods.
- Achieved millisecond-level latency in autonomous driving scenarios.
Conclusions:
- The proposed hardware enables physical-level spatiotemporal fusion.
- This approach can fundamentally reshape machine vision architectures.
- Potential for extensions to real-time decision systems.
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