Neuromorphic Systems: Devices, Architecture, and Algorithms

Abstract

The application of the structure and principles of the human brain opens up great opportunities for creating artificial systems based on silicon technology. The energy efficiency and performance of a biosimilar architecture can be significantly higher compared to the traditional von Neumann architecture. This paper presents an overview of the most promising artificial neural network (ANN) and spiking neural network (SNN) architectures for biosimilar systems, called neuromorphic systems. Devices for biosimilar systems, such as memristors and ferroelectric transistors, are considered for use as artificial synapses that determine the possibility of creating various architectures of neuromorphic systems; methods and rules for training structures to work correctly when mimicking biological learning rules, such as long-term synaptic plasticity. Problems hindering the implementation of biosimilar systems and examples of architectures that have been practically implemented are discussed.