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Mitigating and Tolerating Read Disturbance in STT-MRAM-Based Main Memory via Device and Architecture Innovations.

, , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 38 (12): 2229-2242 (2019)

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Two-level soft error vulnerability prediction on SMT/CMP architectures., , and . IISWC, page 78. IEEE Computer Society, (2011)Optimizing Cloud Data Center Energy Efficiency via Dynamic Prediction of CPU Idle Intervals., , and . CLOUD, page 985-988. IEEE Computer Society, (2015)Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read., , , and . ASP-DAC, page 31-36. IEEE, (2016)FlowPaP and FlowReR: Improving Energy Efficiency and Performance for STT-MRAM-Based Handheld Devices under Read Disturbance., , , , and . ACM Trans. Embed. Comput. Syst., 16 (5s): 132:1-132:20 (2017)Enabling NVM-based deep learning acceleration using nonuniform data quantization: work-in-progress., , and . CASES, page 20:1-20:2. ACM, (2017)Efficient Microarchitectural Vulnerabilities Prediction Using Boosted Regression Trees and Patient Rule Inductions., , and . IEEE Trans. Computers, 59 (5): 593-607 (2010)iCELIA: A Full-Stack Framework for STT-MRAM-Based Deep Learning Acceleration., , , , and . IEEE Trans. Parallel Distributed Syst., 31 (2): 408-422 (2020)Optimal microarchitectural design configuration selection for processor hard-error reliability., , , and . ISQED, page 91-96. IEEE, (2012)CELIA: A Device and Architecture Co-Design Framework for STT-MRAM-Based Deep Learning Acceleration., , , and . ICS, page 149-159. ACM, (2018)Designing and evaluating hybrid storage for high performance cloud computing., , and . SysCon, page 1-8. IEEE, (2018)