CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning
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DOI: 10.1038/s41467-024-46645-6
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References listed on IDEAS
- Takuya Funatsu & Shun Kanai & Jun’ichi Ieda & Shunsuke Fukami & Hideo Ohno, 2022. "Local bifurcation with spin-transfer torque in superparamagnetic tunnel junctions," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
- Kyung Seok Woo & Jaehyun Kim & Janguk Han & Woohyun Kim & Yoon Ho Jang & Cheol Seong Hwang, 2022. "Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
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- Lennart Dabelow & Masahito Ueda, 2022. "Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
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- John Daniel & Zheng Sun & Xuejian Zhang & Yuanqiu Tan & Neil Dilley & Zhihong Chen & Joerg Appenzeller, 2024. "Experimental demonstration of an on-chip p-bit core based on stochastic magnetic tunnel junctions and 2D MoS2 transistors," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
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