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Showing 1–10 of 10 results
Advanced filters: Author: Yunning Li Clear advanced filters

  • A hafnium oxide memristor crossbar array integrated with transistors can provide a provable key destruction scheme in which unique physical fingerprints are extracted by comparing the conductance of neighbouring memristors, and can only be revealed if a digital key stored on the same array is erased.

    • Hao Jiang
    • Can Li
    • Qiangfei Xia
    Research
    Nature Electronics
    Volume: 1, P: 548-554

  • Memristor crossbars with array sizes of up to 128 × 64 cells are capable of analogue vector-matrix multiplication and can be used for signal processing, image compression and convolutional filtering.

    • Can Li
    • Miao Hu
    • Qiangfei Xia
    Research
    Nature Electronics
    Volume: 1, P: 52-59

  • Memristive devices can provide energy-efficient neural network implementations, but they must be tailored to suit different network architectures. Wang et al. develop a trainable weight-sharing mechanism for memristor-based CNNs and ConvLSTMs, achieving a 75% reduction in weights without compromising accuracy.

    • Zhongrui Wang
    • Can Li
    • J. Joshua Yang
    Research
    Nature Machine Intelligence
    Volume: 1, P: 434-442

  • Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.

    • Can Li
    • Zhongrui Wang
    • Qiangfei Xia
    Research
    Nature Machine Intelligence
    Volume: 1, P: 49-57

  • A three-dimensional circuit composed of eight layers of monolithically integrated memristive devices is built and used to implement complex neural networks, demonstrating accurate MNIST classification and effective edge detection in videos.

    • Peng Lin
    • Can Li
    • Qiangfei Xia
    Research
    Nature Electronics
    Volume: 3, P: 225-232

  • Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.

    • Can Li
    • Daniel Belkin
    • Qiangfei Xia
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-8

  • Memristors can switch between high and low electrical-resistance states, but the switching behaviour can be unpredictable. Here, the authors harness this unpredictability to develop a memristor-based true random number generator that uses the stochastic delay time of threshold switching

    • Hao Jiang
    • Daniel Belkin
    • Qiangfei Xia
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-9

  • Though memristors can potentially emulate neuron and synapse functionality, useful signal energy is lost to Joule heating. Here, the authors demonstrate neuro-transistors with a pseudo-memcapacitive gate that actively process signals via energy-efficient capacitively-coupled neural networks.

    • Zhongrui Wang
    • Mingyi Rao
    • J. Joshua Yang
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-10

  • A reinforcement learning algorithm can be implemented on a hybrid analogue–digital platform based on memristive arrays for parallel and energy-efficient in situ training.

    • Zhongrui Wang
    • Can Li
    • J. Joshua Yang
    Research
    Nature Electronics
    Volume: 2, P: 115-124