Design of digital accelerators for backpropagation
This paper proposes an efficient technique for implementing artificial neural networks (ANNs). This technique is utilized to design two fast neurocomputers; FMAT1 and FMAT2. The paper concentrates on the recall and learning phases of multilayer perceptrons with backpropagation learning. FMAT1 requires less hardware but is appropriate for the recall phase only. With a small additional cost, FMAT2 adds the capability of learning. When compared to other techniques in the literature, FMAT1 and FMAT2 exhibit superior performance. They provide a better connections per unit time measure. To compute a neural network having N Neurons in its largest layer, These two architectures require O(log N) processing time. Another major virtue of these architectures is their ability to pipeline multiple patterns which further improves performance.