A Neuron Library for Rapid Realization of Artificial Neural Networks on FPGA: A Case Study of Rössler Chaotic System.

Artificial neural networks (ANNs) are implemented in hardware when software implementations are inadequate in terms of performance. Implementing an ANN as hardware without using design automation tools is a time consuming process. On the other hand, this process can be automated using pre-designed neurons. Thus, in this work, several artificial neural cells were designed and implemented to form a library of neurons for rapid realization of ANNs on FPGA-based embedded systems. The library contains a total of 60 different neurons, two-, four- and six-input biased and non-biased, with each having 10 different activation functions. The neurons are highly pipelined and were designed to be connected to each other like Lego pieces. Chip statistics of the neurons showed that depending on the type of the neuron, about 25 selected neurons can be fit in to the smallest Virtex-6 chip and an ANN formed using the neurons can be clocked up to 576.89MHz. ANN based Rössler system was constructed to show the effectiveness of using neurons in rapid realization of ANNs on embedded systems. Our experiments with the neurons showed that using these neurons, ANNs can rapidly be implemented as hardware and design time can significantly be reduced. [ABSTRACT FROM AUTHOR]