Parallel Implementations of Back Propagation Networks on a Dynamic Data-Driven Multiprocessor

概要

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The data-driven model of computation is well suited for flexible and highly parallel simulation of neural networks. First, the operational semantics of data-driven languages preserve the locality and functionality of neural networks, and naturally describe their inherent parallelism. Second, the asynchronous data-driven execution facilitates the implementation of large and scalable multiprocessor systems, which are necessary to obtain considerable degrees of simulation speedups. In this paper, we present a dynamic data-driven multiprocessor system, and demonstrate its suitability for the parallel simulation of back propagation neural networks. Two parallel implementations are described and evaluated using an image data compression network. The system is scalable, and as a result, the performance improved proportionally with the increase in number of processors.
社団法人電子情報通信学会の論文
1994-05-25