| Autor: |
Andrew W. Stephan, Qiuwen Lou, Michael T. Niemier, Xiaobo Sharon Hu, Steven J. Koester |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 5, Iss 2, Pp 67-73 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2329-9231 |
| DOI: |
10.1109/JXCDC.2019.2932992 |
| Popis: |
A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive READ is proposed. The cells can be used as neural computing units. A dual-circuit neural network architecture is proposed to leverage these devices against the complex operations involved in convolutional networks. Simulations based on HSPICE and MATLAB were performed to study the performance of this architecture when classifying images as well as the effect of varying the size and stability of the nanomagnets. The spintronic cells outperform a purely charge-based implementation of the same network, consuming ≈100-pJ total energy per image processed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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