Report
Quantum materials for energy-efficient neuromorphic computing
| العنوان: | Quantum materials for energy-efficient neuromorphic computing |
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| المؤلفون: | Hoffmann, Axel, Ramanathan, Shriram, Grollier, Julie, Kent, Andrew D., Rozenberg, Marcelo, Schuller, Ivan K., Shpyrko, Oleg, Dynes, Robert, Fainman, Yeshaiahu, Frano, Alex, Fullerton, Eric E., Galli, Giulia, Lomakin, Vitaliy, Ong, Shyue Ping, Petford-Long, Amanda K., Schuller, Jonathan A., Stiles, Mark D., Takamura, Yayoi, Zhu, Yimei |
| المصدر: | APL Materials 10, 070904 (2022) |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Emerging Technologies, Condensed Matter - Materials Science, Computer Science - Neural and Evolutionary Computing, Physics - Applied Physics |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1063/5.0094205 |
| URL الوصول: | http://arxiv.org/abs/2204.01832 |
| رقم الانضمام: | edsarx.2204.01832 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1063/5.0094205 |
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