Neural network detector with sparse codes for spin transfer torque magnetic random access memory
نویسندگان
چکیده
This paper presents leveraging the neural network detector to improve performance of a spin transfer torque magnetic random-access memory (STT-MRAM), where sparse coding scheme is also applied protect user data for asymmetric write failure. The STT-MRAM has recently emerged as good candidate, attracting attention both academia and industry because its unique features such density, non-volatility, power consumption, integration with CMOS technology. reliability degraded significantly by reading writing failures. failure primary contributor errors in STT-MRAM. issue arises from higher error rate when switching 0 (low resistance) 1 (high resistance), compared 0. can be employed, which ensures that codeword weight always less than half length. By reducing frequency encountering “1,” this method minimizes occurrence ➔ switching, turn reduces system. offers better threshold tackles system’s unknown offset. Simulation results show proposed provide improvements under effect read errors. For instance, model handle approximately 9%, whereas traditional thresholding solution only allows about 6%. Moreover, proposal eliminates propagation retrieving original information.
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ژورنال
عنوان ژورنال: Cogent engineering
سال: 2023
ISSN: ['2331-1916']
DOI: https://doi.org/10.1080/23311916.2023.2231724