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Neural Architecture Search with In‐Memory Multiply–Accumulate and In‐Memory Rank Based on Coating Layer Optimized C‐Doped Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> Phase Change Memory (Adv. Funct. Mater. 15/2024).
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- Advanced Functional Materials, 2024, v. 34, n. 15, p. 1, doi. 10.1002/adfm.202300458
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Neural Architecture Search with In‐Memory Multiply–Accumulate and In‐Memory Rank Based on Coating Layer Optimized C‐Doped Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> Phase Change Memory.
- Published in:
- Advanced Functional Materials, 2024, v. 34, n. 15, p. 1, doi. 10.1002/adfm.202300458
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- Article
Resistive Memory‐Based In‐Memory Computing: From Device and Large‐Scale Integration System Perspectives.
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- Advanced Intelligent Systems (2640-4567), 2019, v. 1, n. 7, p. N.PAG, doi. 10.1002/aisy.201900068
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Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning.
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- Nature Communications, 2023, v. 14, n. 1, p. 1, doi. 10.1038/s41467-023-42981-1
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In‐Memory Realization of Eligibility Traces Based on Conductance Drift of Phase Change Memory for Energy‐Efficient Reinforcement Learning.
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- Advanced Materials, 2022, v. 34, n. 6, p. 1, doi. 10.1002/adma.202107811
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- Article