Practical Implementation of Memristor-Based Threshold Logic Gates

TL;DR

This paper presents a practical implementation of memristor-based threshold logic gates (TLGs) that leverage ReRAM technology for reconfigurable, low-power, high-speed logic suitable for neural network applications.

Contribution

It introduces a physical memristor-based current-mode TLG design, demonstrating reconfigurability and potential for in-memory neural network hardware.

Findings

Successfully implemented 2-input and 3-input memristor-based TLGs

Demonstrated reconfigurable weights for classification tasks

Showed potential for low-power, high-speed neural network hardware

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; benefits which follow from its technological attributes, such as nanoscale dimensions, low power operation and multi-state programming. At the same time, design with CMOS is quickly reaching its physical and functional limitations, and further research towards novel logic families, such as Threshold Logic Gates (TLGs) is scoped. TLGs constitute a logic family known for its high-speed and low power consumption, yet rely on conventional transistor technology. Introducing memristors enables a more affordable reconfiguration capability of TLGs. Through this work, we are introducing a physical implementation of a memristor-based current-mode TLG (MCMTLG) circuit and validate its design and operation through multiple experimental setups. We demonstrate 2-input and 3-input MCMTLG configurations and showcase their reconfiguration capability. This is achieved by varying memristive weights arbitrarily for shaping the classification decision boundary, thus showing promise as an alternative hardware-friendly implementation of Artificial Neural Networks (ANNs). Through the employment of real memristor devices as the equivalent of synaptic weights in TLGs, we are realizing components that can be used towards an in-silico classifier.