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Showing 1–4 of 4 results
Advanced filters: Author: Yudeng Lin Clear advanced filters

  • The stochastic features of memristors make them suitable for computation and probabilistic sampling; however, implementing these properties in hardware is extremely challenging. Lin et al. introduce an approach that leverages the cycle-to-cycle read variability of memristors as a physical random variable for in situ, real-time random number generation, and demonstrate it on a risk-sensitive reinforcement learning task.

    • Yudeng Lin
    • Qingtian Zhang
    • Huaqiang Wu
    Research
    Nature Machine Intelligence
    Volume: 5, P: 714-723

  • This study introduces an in-memory deep Bayesian active learning framework that uses the stochastic properties of memristors for in situ probabilistic computations. This framework can greatly improve the efficiency and speed of artificial intelligence learning tasks, as demonstrated with a robot skill-learning task.

    • Yudeng Lin
    • Bin Gao
    • Huaqiang Wu
    ResearchOpen Access
    Nature Computational Science
    Volume: 5, P: 27-36

  • Image reconstruction algorithms raise critical challenges in massive data processing for medical diagnosis. Here, the authors propose a solution to significantly accelerate medical image reconstruction on memristor arrays, showing 79× faster speed and 153× higher energy efficiency than state-of-the-art graphics processing unit.

    • Han Zhao
    • Zhengwu Liu
    • Huaqiang Wu
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-10