Artificial Neural Networks on Reconfigurable Meshes

Artificial neural networks(ANN) have been used successfully in applications such as pattern recognition, image processing, automation and control. Majority of today’s applications use backpropagate feedforward ANN. In this paper, two methods of P pattern L layer ANN learning on n x n RMESH have been presented. One required memory space of O(nL) but conceptually is simpler to develop and the other uses pipelined approach which reduces the memory requirement to O(L). Both of these algorithms take O(PL) time and are optimal for RMESH architecture

Feedforward Backpropagation Artificial Neural Networks on Reconfigurable Meshes

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. Majority of today\u27s applications use backpropagate feedforward ANN. In this paper, two methods of P pattern L layer ANN learning on n × n RMESH have been presented. One required memory space of O(nL) but conceptually is simpler to develop and the other uses pipelined approach which reduces the memory requirement to O(L). Both of these algorithms take O(PL) time and are optimal for RMESH architecture