Bio-Inspired Vision Processor for Ultra-Fast Object Categorization

We present a scalable hardware architecture to implement large-scale bio-inspired synthetic vision systems. The system is a fully digital implementation of a modular vision engine that can perform real-time detection, recognition and segmentation of mega-pixel images. We present performance comparisons between software versions of the vision system executing on CPU and GPU machines, and show that our FPGA implementation can outperform these systems by a factor of four.

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