Quantum materials for energy-efficient neuromorphic computing
Abstrak
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.
Topik & Kata Kunci
Penulis (19)
Axel Hoffmann
Shriram Ramanathan
Julie Grollier
Andrew D. Kent
Marcelo Rozenberg
Ivan K. Schuller
Oleg Shpyrko
Robert Dynes
Yeshaiahu Fainman
Alex Frano
Eric E. Fullerton
Giulia Galli
Vitaliy Lomakin
Shyue Ping Ong
Amanda K. Petford-Long
Jonathan A. Schuller
Mark D. Stiles
Yayoi Takamura
Yimei Zhu
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓