Efficient FPGA Realization of the Memristive Wilson Neuron Model in the Face of Electromagnetic Interference

Hardware implementation of new neuron models or improved conventional neuron models has made a significant contribution to neuromorphic development. One of the important factors considered to improve the conventional neuron models is to explore the impact of electromagnetic energy on neurons. In this work the efficient FPGA implementation of memristive Wilson (MW) neuron model using two approximate MW model is presented. For the first approximate MW (AMW1) model in a hybrid method, piecewise linear (PWL) and CORDIC functions have been used to provide a multiplierless and accurate model. The PWL approximation method is used to provide the second approximate MW (AMW2) model. Results of the FPGA implementation for both the MW and AMW models illustrate that, the AMW1 model with an overall saving of 79%, and the AMW2 model with an overall saving of 69% are appropriate options for large scale implementations. The average NRMSE for the AMW1 model is 0.57%, while for the AMW2 model it is 1.23%. The maximum frequency of AMW2 model is 91.5% better than AMW1 model and realizes high frequency implementation.