A low-power neurosynaptic implementation of Local Binary Patterns for texture analysis

Abstract

We demonstrate how to map Local Binary Patterns (LBP), a class of leading feature extractors, onto a neuromorphic processor such as TrueNorth, a silicon expression of a non-von Neumann, low-power, spiking-based, brain-inspired processor. The application is presented in the form of a texture feature extractor that can process 8-bit grayscale video at 30fps. While consuming less than 140mW of power, this neuromorphic implementation provides a rotation and contrast insensitive characterization of texture, with similar accuracy as a standard von Neumann implementation of the same algorithm. The successful mapping of an important vision routine on a neuromorphic architecture is indicative of an alternative paradigm for addressing the von Neumann bottleneck, which is currently placing severe constraints on the processing speed, power consumption, reliability, scalability, programmability and mobility of vision algorithms. This also introduces a new methodology for the design of vision algorithms for power efficient, asynchronous, mobility-targeted applications.