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Ariel: a scalable multiprocessor for the simulation of neural networks

Ariel is a multiprocessor architecture that we are developing to simulate neural networks and other models of distributed computation. The design is based upon a hierarchical network of coarse-grained processing modules. The module hardware uses fast digital signal processors and very large semiconductor memories to provide the throughput and storage capacity required to simulate large networks. Our objective is to provide a system that can be scaled up to simulate neural networks composed of millions of nodes and 10s of billions of interconnections at rates exceeding 100 billion connection operations per second. This paper discusses the technical challenges in neural network simulation and describes Ariel's major components.