An efficient parallel block backpropagation learning algorithm in transputer-based mesh-connected parallel computers
Learning process is essential for good performance when a neural network is applied to a practical application. The backpropagation algorithm [1] is a well-known learning method widely used in most neural networks. However. since the backpropagation algorithm is time-consuming, much research have been done to speed up the process. The block backpropagation algorithm. which seems to be more efficient than the backpropagation, is recently proposed by Coetzee in [2]. In this paper, we propose an efficient parallel algorithm fur the block backpropagation method and its performance model in mesh-connected parallel computer systems. The proposed algorithm adopts master-slave model for weight broadcasting and data parallelism for computation of weights. In order to validate our performance model. a neural network is implemented for printed character recognition application in the TiME [3] which is a prototype parallel machine consisting of 32 transputers connected in mesh topology. It is shown that speedup by our performance model is very close to that by experiments.
block backpropagation transputer parallel computing load balancing