A CNN-driven locally adaptive CMOS image sensor

A bioinspired model for mixed-signal array mimics the way in which images are processed in the visual pathway. Focal-plane processing of images permits local adaptation of photoreceptor structures in silicon. Beyond simple resistive grid filtering, nonlinear and anisotropic diffusion can be programmed in this CNN chip. This paper presents the local circuitry for sensors adaptation based on the mixed-signal VLSI parallel processing infrastructure in CMOS

A Focal-Plane Image Processor for Low Power Adaptive Capture and Analysis of the Visual Stimulus

Portable applications of artificial vision are limited by the fact that conventional processing schemes fail to meet the specifications under a tight power budget. A bio-inspired approach, based in the goal-directed organization of sensory organs found in nature, has been employed to implement a focal-plane image processor for low power vision applications. The prototype contains a multi-layered CNN structure concurrent with 32times32 photosensors with locally programmable integration time for adaptive image capture with on-chip local and global adaptation mechanisms. A more robust and linear multiplier block has been employed to reduce irregular analog wave propagation ought to asymmetric synapses. The predicted computing power per power consumption, 142MOPS/mW, is orders of magnitude above what rendered by conventional architectures