| Autor: |
Huang, Puyang, Gu, Yu, Fu, Chenyi, Lu, Jiaqi, Zhu, Yiyao, Chen, Renhe, Hu, Yongqi, Ding, Yi, Zhang, Hongchao, Lu, Shiyang, Peng, Shouzhong, Zhao, Weisheng, Kou, Xufeng |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware. |
| Databáze: |
arXiv |
| Externí odkaz: |
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