Practical Implementation of Memristor-Based Threshold Logic Gates

Abstract

Current advances in emerging memory technologies enable novel and unconventional computing architectures for high-performance and low-power electronic systems, capable of carrying out massively parallel operations at the edge. One emerging technology, ReRAM, also known to belong in the family of memristors (memory resistors), is gathering attention due to its attractive features for logic and in-memory computing and benefits which follow from its technological attributes, such as nanoscale dimensions, low-power operation, and multi-state programming. At the same time, the design with CMOS is quickly reaching its physical and functional limitations, and further research toward novel logic families, such as threshold logic gates (TLGs), is scoped. In this paper, we introduce a physical implementation of a memristor-based current-mode TLG (MCMTLG) circuit and validate its design and operation through multiple experimental setups. We demonstrate two-input, three-input, and four-input MCMTLG configurations and showcase their reconfiguration capability. This is achieved by varying memristive weights arbitrarily for shaping the classification decision boundary, thus showing a promise as an alternative hardware-friendly implementation of artificial neural networks. Through the employment of real memristor devices as the equivalent of synaptic weights in TLGs, we are realizing components that can be used toward an in silico classifier.