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Showing 1–3 of 3 results
Advanced filters: Author: Shuangzhu Tang Clear advanced filters

  • This study introduces an error-aware probabilistic update method (EaPU) that aligns stochastic analog compute-in-memory device behavior with neural network training dynamics, reducing weight updates and energy consumption.

    • Jinchang Liu
    • Jian Lu
    • Qi Liu
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-13

  • Emerging in-memory computing using memristors promises better energy efficiency but often focuses on existing AI algorithms. Here, the authors report a distinct and computation-efficient AI algorithm based on memristor drift-diffusion kinetics for pattern classification, implemented on 180 nm memristor chips.

    • Tuo Shi
    • Lili Gao
    • Qi Liu
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-13

  • The separation of sensor, memory, and processor in a recognition system deteriorates the latency of decision-making and increases the overall computing power. Here, Zhang et al. develop a photoelectronic reservoir computing system, consisting of DUV photo-synapses and a memristor array, to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing.

    • Zhongfang Zhang
    • Xiaolong Zhao
    • Shibing Long
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-9