Design of Low-Cost, Real-Time Simulation Systems for Large Neural Networks

Systems with large amounts of computing power and storage are required to simulate very large neural networks capable of tackling complex control problems and real-time emulation of the human sensory, language and reasoning systems. General-purpose parallel computers do not have communications, processor and memory architectures optimized for neural computation and so can not perform such simulations at reasonable cost. This thesis analyses several software and hardware strategies to make feasible the simulation of large, brain-like neural networks in real-time, and presents a particular multicomputer design able to implement these strategies. An important design goal is that the system must not sacrifice computational flexibility for speed, as new information about the workings of the brain and new artificial neural network architectures and learning algorithms are continually emerging. The main contributions of the thesis are: — an analysis of the important features of biological neural networks that need to be simulated, — a review of hardware and software approaches to neural networks, and an evaluation of their abilities to simulate brain-like networks, — the development of techniques for efficient simulation of brain-like neural networks, and — the description of a multicomputer that is able to simulate large, brain-like neural networks in real-time and at low cost. Part of this work will be published in the Journal of Parallel and Distributed Computing, Special Issue on Neural Computing on Massively Parallel Processors, March 1992. ADDRESS FOR CORRESPONDENCE: Mark James Tel : +61-2-692-4276 Basser Department of Computer Science : +61-2-692-3423 Madsen Building, F09 Fax : +61-2-692-3838 The University of Sydney NSW 2006 Email : [email protected] AUSTRALIA