Mapping of backpropagation learning onto distributed memory multiprocessors
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abstract
This paper presents a mapping scheme for parallel pipelined execution of the Backpropagation Learning Algorithm on distributed memory multiprocessors (DMMs). The proposed implementation exhibits training set parallelism that involves batch updating. Simple algorithms have been presented, which allow the data transfer involved in both forward and backward executions phases of the backpropagation algorithm to be carried out with a small communication overhead. The effectiveness of our mapping has been illustrated, by estimating the speedup of a proposed implementation on an array of T-805 transputers.
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Proceedings 1st International Conference on Algorithms and Architectures for Parallel Processing
