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In spite of tremendous advances in data dependence and dataflow analysis techniques, state-of-the-art optimizing compilers continue to suffer from imprecisions and miss potential optimization opportunities. These imprecisions result from statically unknown

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In this paper, we present a new technique for mapping the backpropagation learning algorithm on a mesh signal processor. The optimal sub-partitioning of computation and communication, and data replicaiion techniques are the key features of our algorithm. Theoretical analysis and simulation results, using the M I T Lincoln, Lab simulator, show that our scheme performs better than the other schemes.