Memristive devices for computing

Yang, J. Joshua, Strukov, Dmitri B., Stewart, Duncan R.·2013·Nature Nanotechnology
DOI 10.1038/nnano.2012.240

Cited by (1,007)

Items 1,001–1,007 of 1,007

  1. Nonideality‐Aware Training for Accurate and Robust Low‐Power Memristive Neural Networks
    Joksas, Dovydas, Wang, Erwei, Barmpatsalos, Nikolaos, Ng, Wing H., Kenyon, Anthony J., Constantinides, George A., Mehonic, Adnan · 2022 · Advanced Science
  2. Comprehensive Compact Phenomenological Modeling of Integrated Metal-Oxide Memristors
    Nili, Hussein, Vincent, Adrien F., Prezesio, Mirko, Mahmoodi, Mohammad R., Kataeva, Irina, Strukov, Dmitri B. · 2020 · IEEE Transactions on Nanotechnology
  3. Brain-inspired computing systems: a systematic literature review
    Zolfagharinejad, Mohamadreza, Alegre-Ibarra, Unai, Chen, Tao, Kinge, Sachin, van der Wiel, Wilfred G. · 2024 · The European Physical Journal B
  4. Framework for In-Memory Computing Based on Memristor and Memcapacitor for On-Chip Training
    Singh, Ankur, Lee, Byung-Geun · 2023 · IEEE Access
  5. Improving the Recognition Accuracy of Memristive Neural Networks via Homogenized Analog Type Conductance Quantization
    Chen, Qilai, Han, Tingting, Tang, Minghua, Zhang, Zhang, Zheng, Xuejun, Liu, Gang · 2020 · Micromachines
  6. A memristive computational neural network model for time-series processing
    Pistolesi, Veronica, Ceni, Andrea, Milano, Gianluca, Ricciardi, Carlo, Gallicchio, Claudio · 2025 · APL Machine Learning
  7. 2022 roadmap on neuromorphic computing and engineering
    Christensen, Dennis V, Dittmann, Regina, Linares-Barranco, Bernabe, Sebastian, Abu, Le Gallo, Manuel, Redaelli, Andrea, Slesazeck, Stefan, Mikolajick, Thomas, Spiga, Sabina, Menzel, Stephan et al. · 2022 · Neuromorphic Computing and Engineering