Performance Evaluation Of Neural Network-Based Channel Detection For STT-MRAM

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.