Optimization dynamics for partitioned neural networks.
Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.
Tsioutsias
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Yale University, New Haven, CT 06520-8285, USA.
Mjolsness
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D007700:Kinetics; D008433:Mathematics; D008959:Models, Neurological; D008962:Models, Theoretical; D016571:Neural Networks, Computer