Analytical Calculation of Inference in Memristor-based Stochastic Artificial Neural Networks

N. Bogun (THM University of Applied Sciences and Justus-Liebig Univ., Germany), E. Perez-Bosch Quesada, E. Perez (IHP-Leibniz-Institut für Innovative Mikroelektronik, Germany), C. Wenger (IHP-Leibniz-Institut für Innovative Mikroelektronik and BTU Cottbus-Senftenberg, Germany), A. Kloes, M. Schwarz (THM University of Applied Sciences, Germany)

The impact of artificial intelligence on human life has increased significantly in recent years. However, as the complexity of problems rose aswell, increasing system features for such amount of data computation became troublesome due to the von Neumann’s computer architecture. Neuromorphic computing aims to solve this problem by mimicking the parallel computation of a human brain. For this approach, memristive devices are used to emulate the synapses of a human brain. Yet, common simulations of hardware based networks require time consuming Monte-Carlo simulations to take into account the stochastic switching of memristive devices. This work presents an alternative concept making use of the convolution of the probability distribution functions (PDF) of memristor currents by its equivalent multiplication in Fourier domain. An artificial neural network is accordingly implemented to perform the inference stage with handwritten digits.