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Abstract
The recurrent back-propagation algorithm for neural networks has been implemented on the Connection Machine, a massively parallel processor. Two fundamentally different graph architectures underlying the nets were tested: one based on arcs, the other on nodes. Confirming the predominance of communication over computation, performance measurements underscore the necessity to make connections the basic unit of representation. Comparisons between these graph algorithms lead to important conclusions concerning the parallel implementation of neural nets in both software and hardware.
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