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- W4210984978 abstract "In this study, we evaluate the performance of neural network-based channel detection under the support of spares coding for spin-torque transfer magnetic random access memory (STT-MRAM). Due to its unique features, such as high density, high endurance, and high-speed input/output, the STT-MRAM is considered to have a significant opportunity in the consumer electronics market for the Internet of Things (IoT) field and artificial intelligence (AI) applications. Yet, the reliability of STT-MRAM is significantly degraded due to the influence of both write and read errors. A proposed scheme that the user signal is encoded by sparse codes and detected by the RNN-based detector is evaluated in this paper. Improvements over the conventional detection are shown through simulation results." @default.
- W4210984978 created "2022-02-13" @default.
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- W4210984978 date "2021-12-21" @default.
- W4210984978 modified "2023-09-30" @default.
- W4210984978 title "Performance Evaluation Of Neural Network-Based Channel Detection For STT-MRAM" @default.
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- W4210984978 doi "https://doi.org/10.1109/nics54270.2021.9701555" @default.
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