UCLA PUNNS --A Neural Network Machine For Computer Vision

Abstract

The sequential processing paradigm limits current solutions for computer vision by restricting the number of functions which naturally map onto Von Neumann computing architectures. A variety of physical computing structures underlie the massive parallelism inherent in many visual functions. Therefore, further advances in general purpose vision must assume inseparability of function from structure. To combine function and structure we are investigating connectionist architectures using PUNNS (Perception Using Neural Network Simulation). Our approach is inspired and constrained by the analysis of visual functions that are computed in the neural networks of living things. PUNNS represents a massively parallel computer architecture which is evolving to allow the execution of certain visual functions in constant time, regardless of the size and complexity of the image. Due to the complexity and cost of building a neural net machine, a flexible neural net simulator is needed to invent, study and understand the behavior of complex vision algorithms. Some of the issues involved in building a simulator are how to compactly describe the interconnectivity of the neural network, how to input image data, how to program the neural network, and how to display the results of the network. This paper describes the implementation of PUNNS. Simulation examples and a comparison of PUNNS to other neural net simulators will be presented.