Parallel implementation of backpropagation on transputers
Shou King Foo, P. Saratchandran, N. Sundararajan · 2005
Backpropagation algorithm is one of the most popular training algorithms for multilayer feedforward neural networks. However training the network with this algorithm has proved to be computationally intensive for a sequential machine. In this paper, parallel implementation of the backpropagation algorithm is investigated using transputers hosted by a personal computer. Two methods of transputer implementations were considered. One method was the multi-tasking approach and the other the processor farming approach. Results showed that for all test cases, the training time for the neural network with the multi-tasking approach is shorter than the processor farming approach. Comparing with a serial 486-33 PC, it is found that as the problem size scales up, the improvement in training time from the parallel implementation becomes significant.