Design of low-cost, real-time simulation systems for large neural networks

Abstract

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 cannot perform such simulations at reasonable cost. This paper analyzes several software and hardware strategies to make feasible the simulation of large 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.