CONCURRENT NEUROMORPHIC ALGORITHMS FOR OPTIMIZATION OF THE COMPUTATIONAL LOAD OF A HYPERCUBE SUPERCOMPUTER.

J. Barhen, N. Toomarian, V. Protopopescu, M. Clinard

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

A novel approach to the optimization of the time-dependent computational load in message-passing multiprocessor systems is presented. Compact neuromorphic data structures are used to model effects such as precedence constraints, processor idling time, and task-schedule overlap. Analytic expressions are given for the effect of single-neuron perturbations on the systems' configuration energy. Algorithms for the implementation of this methodology on a hypercube supercomputer are outlined along with potental extensions to large-scale nonlinear asynchronous neural networks.

Original languageEnglish
Pagesiv/687-696
StatePublished - 1987

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