Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing

TLDR

Neuromorphic computing emulates brain neurons for energy‑efficient, fast neural networks, but memristor‑based implementations are limited by reliability issues. The study demonstrates a transistor‑free 1R memristor cross‑bar array that offers low variation, 100 % yield, large dynamic range, and fast speed for artificial neurons and neuromorphic computing. The authors built a transistor‑free 1R memristor cross‑bar array and leveraged its short‑term memory effect to construct a neuro‑memristive system for efficient sequential data processing. The memristor array enabled a reliable leaky‑integrate‑and‑fire neuron and a neuro‑memristive system that trains and generates antimicrobial peptide sequences with few parameters, demonstrating the feasibility of memristor‑based neuromorphic computing for energy‑efficient edge devices.

Abstract

Abstract Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor’s non-reliability issues have been one of the main obstacles for the development of memristor-based artificial neurons and neuromorphic computings. Here, we show a memristor 1R cross-bar array without transistor devices for individual memristor access with low variation, 100% yield, large dynamic range, and fast speed for artificial neuron and neuromorphic computing. Based on the developed memristor, we experimentally demonstrate a memristor-based neuron with leaky-integrate and fire property with excellent reliability. Furthermore, we develop a neuro-memristive computing system based on the short-term memory effect of the developed memristor for efficient processing of sequential data. Our neuro-memristive computing system successfully trains and generates bio-medical sequential data (antimicrobial peptides) while using a small number of training parameters. Our results open up the possibility of memristor-based artificial neurons and neuromorphic computing systems, which are essential for energy-efficient edge computing devices.