Quantum materials for energy-efficient neuromorphic computing

TL;DR

This paper explores how quantum materials with unique non-linear properties can enable energy-efficient neuromorphic computing hardware, highlighting recent approaches, opportunities, and challenges in developing quantum-material-based neuromorphic devices.

Contribution

It presents a perspective on utilizing quantum materials' non-linear responses for neuromorphic hardware, emphasizing recent examples and future challenges.

Findings

Quantum materials enable energy-efficient neuromorphic device concepts.

Strong correlations in quantum materials lead to non-linear responses useful for plasticity.

Magnetization dynamics in quantum materials can be used for data classification.

Abstract

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.