Memcapacitive Devices in Logic and Crossbar Applications

TL;DR

This paper introduces memcapacitive devices for logic and crossbar applications, demonstrating significant power savings and comparable performance to memristive architectures through novel device design and simulation.

Contribution

The paper presents the first memcapacitive logic gates and crossbar classifiers, showing they outperform memristor-based systems in power efficiency while maintaining similar accuracy.

Findings

Memcapacitive gates consume about 7x less power than memristive gates.

Memcapacitive crossbar classifiers reduce power consumption by 1,500x on MNIST.

Devices show potential for low-power Boolean and analog applications.

Abstract

Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits, reducing the energy consumption is limited by the resistive nature of the devices. Memcapacitors would address that limitation while still having all the benefits of memristors. Recent work has shown that with adjusted parameters during the fabrication process, a metal-oxide device can indeed exhibit a memcapacitive behavior. We introduce novel memcapacitive logic gates and memcapacitive crossbar classifiers as a proof of concept that such applications can outperform memristor-based architectures. The results illustrate that, compared to memristive logic gates, our memcapacitive gates consume about 7x less power. The memcapacitive crossbar classifier achieves similar classification performance but reduces the power consumption by a factor of about 1,500x for the MNIST dataset and a factor of about 1,000x for the CIFAR-10 dataset compared to a memristive crossbar. Our simulation results demonstrate that memcapacitive devices have great potential for both Boolean logic and analog low-power applications.