Quantization analysis and enhancement of a VLSI gradient-based motion estimation architecture

Abstract

Despite more than 40 years of research, motion estimation is still considered an emerging field, a field especially relevant today because of its vast utility for real-world applications. Currently, even the best bio-inspired algorithms lack certain characteristics that are readily found, for example, naturally in, say, mammals. Furthermore, the vast computational resources required are not usually affordable in real-time application. We present here a useful framework for building bio-inspired systems in real-time environments, reducing computational complexity. A complete quantization study of neuromorphic robust optical flow architecture is performed, using properties found in the cortical motion pathway. This architecture is designed for VLSI systems. An extensive analysis is performed to avoid compromising the viability and the robustness of the final system. A set of simulations and techniques that can be helpful for designing real-time artificial vision embedded systems and, specifically, gradient-based optical flow systems is shown. This work includes the final error results, resource usage, and performance data.