Quantum materials for energy-efficient neuromorphic computing
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.
Cite
@article{arxiv.2204.01832,
title = {Quantum materials for energy-efficient neuromorphic computing},
author = {Axel Hoffmann and Shriram Ramanathan and Julie Grollier and Andrew D. Kent and Marcelo Rozenberg and Ivan K. Schuller and Oleg Shpyrko and Robert Dynes and Yeshaiahu Fainman and Alex Frano and Eric E. Fullerton and Giulia Galli and Vitaliy Lomakin and Shyue Ping Ong and Amanda K. Petford-Long and Jonathan A. Schuller and Mark D. Stiles and Yayoi Takamura and Yimei Zhu},
journal= {arXiv preprint arXiv:2204.01832},
year = {2022}
}