An Accuracy Improvement of the Neuromorphic Functional Models by Using the Parallel ANN Architecture

Abstract

© 2020 IEEE. Enhancement of the up-to-date computing systems in performance and memory capacity stimulates development of new mathematical models and methods for numerical simulation. Machine learning methods are widely used nowadays in the electronic design automation. New mathematical entities are focused onto increasing the design quality and reducing a time cost. A method of constructing the neuromorphic functional models (NFM) for analog components and functional blocks is proposed. An approach to improvement of the NFM accuracy by partitioning the domain of definition for output characteristics according to the threshold coefficient and using the parallel artificial neural network (ANN) architecture is offered. The automated synthesis route of the NFM is represented. The results of experimental study for semiconductor diode and the voltage rectifier circuit are demonstrated. The accuracy increasing of the synthesized NFM and circuit simulation results shown high efficiency of the proposed method.