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Mitigating the Effects of RRAM Process Variation on the Accuracy of Artificial Neural Networks.

, , , , , , , и . SAMOS, том 13227 из Lecture Notes in Computer Science, стр. 401-417. Springer, (2021)

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Roberto Francisco Pérez Benito

Luis Alberto Pérez Moya

Héctor Luis Pérez Escalona

Maria Dolores Pérez-Guzmán

Carmen Pérez Samaniego

 

Другие публикации лиц с тем же именем

Behavioral modeling of multilevel HfO2-based memristors for neuromorphic circuit simulation., , , , , , , и . DCIS, стр. 1-6. IEEE, (2020)Mitigating the Effects of RRAM Process Variation on the Accuracy of Artificial Neural Networks., , , , , , , и . SAMOS, том 13227 из Lecture Notes in Computer Science, стр. 401-417. Springer, (2021)One-Transistor-Multiple-RRAM Cells for Energy-Efficient In-Memory Computing., , , , , , , , и . NEWCAS, стр. 1-5. IEEE, (2023)LUT-based RRAM Model for Neural Accelerator Circuit Simulation., , , , , , , , , и 1 other автор(ы). NANOARCH, стр. 35:1-35:6. ACM, (2023)Memristive-based in-memory computing: from device to large-scale CMOS integration., , , и . Neuromorph. Comput. Eng., 1 (2): 24006 (2021)Analytical Calculation of Inference in Memristor-based Stochastic Artificial Neural Networks., , , , , и . MIXDES, стр. 83-88. IEEE, (2022)Comparative Analysis and Optimization of the SystemC-AMS Analog Simulation Efficiency of Resistive Crossbar Arrays., , , , и . DCIS, стр. 1-6. IEEE, (2021)