Data-driven Approximate Edge Detection using Flow-based Computing on Memristor Crossbars

Detection of edges in images is an elementary operation in computer vision that can greatly benefit from an implementation with a low power-delay product. In this paper, we propose a new approach for designing nanoscale memristor crossbars that can implement approximate edge-detection using flow-based computing. Instead of the traditional Boolean approach, our methodology uses a ternary logic approach with three outcomes: True representing an edge, False that representing the absence of an edge, and Don't Care that represents an ambivalent response. Our data-driven design approach uses a corpus of human-labeled edges in order to learn the concept of an edge in an image. A massively parallel simulated annealing search algorithm over 96 processes is used to obtain the design of the memristor crossbar for edge detection. We show that our approximate crossbar design is effective in computing edges of images on the BSD500 benchmark.