An efficient and scalable architecture for neural networks with backpropagation learning

This paper describes the implementation, in reconfigurable hardware, of an artificial neural network (ANN) system architecture which features online supervised learning capabilities and resource virtualization. Neural networks are artificial systems inspired by the brain´s cognitive behavior, which can learn tasks with some degree of complexity, such as, optimization problems, data mining and text and speech recognition. The architecture proposed takes advantage of distinct datapaths for the forward and backward propagation stages to significantly improve the performance of the learning phase. The architecture is easily scalable and able to cope with several network sizes with the same hardware. Networks larger than the available resources are handled by hardware virtualization. The results show that the proposed architecture leads to speed ups of one order of magnitude comparing to high-end software solutions.