A parallel mapping of backpropagation algorithm for mesh signal processor

Abstract

Presents a technique for mapping the backpropagation learning algorithm on a mesh signal processor. The optimal sub-partitioning of computation and communication, and data replication techniques are the key features of the authors' algorithm. Theoretical analysis and simulation results, using the MIT Lincoln Lab simulator, show that the authors' scheme performs better than the other schemes.