Bio-inspired smart vision sensor: toward a reconfigurable hardware modeling of the hierarchical processing in the brain

Biological vision systems inspire processing methods in computer vision applications. This paper employs the insights of vision systems in hardware and presents a pixel-parallel, reconfigurable, and layer-based hierarchical architecture for smart image sensors. The architecture aims to bring computation close to the sensor to achieve high acceleration for different machine vision applications while consuming low power. We logically divide the image into multiple regions and perform pixel-level and region-level processing after removing spatiotemporal redundancy. Those processors use bio-inspired algorithms to activate the regions with region of interest of a scene. The hierarchical processing breaks the traditional sequential image processing and introduces parallelism for machine vision applications. Also, we make the hardware design reconfigurable even after fabrication to make the hardware reusable for different applications. Simulation results show that the area overhead and power penalty for adding reconfigurable features stay in an acceptable range. We emphasize to maximize the operating speed and obtain 800 MHz. Besides, the design saves 84.01% and 96.91% dynamic power at the first and second stages of the hierarchy by removing redundant information. Furthermore, the sequential deployment of high-level reasoning only on the selected regions of the image becomes computationally inexpensive to execute a complex task in real time.

Automated summary

This paper introduces a novel architecture for smart image sensors inspired by biological vision systems, utilizing pixel-parallel and reconfigurable layer-based processing. The hierarchical processing enables parallel computation, reduces redundant information, saves dynamic power, and achieves real-time complex task execution.