Algorithms for building and operation modeling of large electrical circuits with memristor-diode crossbars in a biomorphic neuroprocessor

About the authors:

Abdulla H. Ebrahim, Cand. Sci. (Phys.-Math.), Junior Researcher, Memristive Materials Laboratory, Center for Nature-Inspired Engineering, University of Tyumen, Tyumen, Russia; abdulla.ybragim@mail.ru, https://orcid.org/0000-0002-1709-9882

Sergey Yu. Udovichenko, Dr. Sci. (Phys.-Math.), Professor, Department of Applied and Tech­nical Physics, School of Natural Sciences, University of Tyumen, Tyumen, Russia; Scientific Director of the Memristive Materials Laboratory, Center for Nature-Inspired Engineering, University of Tyumen, Tyumen, Russia; udotgu@mail.ru, https://orcid.org/0000-0003-3583-7081

Abstract:

The biomorphic neuroprocessor is the hardware implementation of the impulse neural network in which incoming information from a set of numbers is converted into impulses, and outgoing information, on the contrary, from impulses into binary code. For the automatic building of electrical circuits of the input coding and output decoding units in neuroprocessor using ultra-large logic matrices based on a memristor-diode crossbar, appropriate algorithms have been developed. For the subsequent imitation modeling of information processing in these units, as well as in the memory matrix of the neuroprocessor, the algorithm for calculating large electrical circuits containing memristor-diode crossbars has been created. This simulation algorithm is based on the well-known algorithm of Simulation Program with Integrated Circuit Emphasis and includes original mathematical models of the memristor and the selective element of the Zener diode, as well as the algorithm for modeling the resistive switching of the memristor. The results of imitation modeling using the developed algorithms and corresponding programs showed the operability of the constructed electrical circuits of the input unit in the mode of encoding a binary number into a impulse frequency by a population of three neurons and the output unit of a neuroprocessor that decodes the impulses coming from neurons into binary format as well as the operability of the memory matrix under weighting and summing impulses. The created algorithms and programs package based on them can be used to effectively solve the engineering and technical problem of manufacturing a biomorphic neuroprocessor that requires modeling of information processing in individual neuroprocessor units based on large memristor-diode arrays in order to optimize their parameters.

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