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Showing 1–4 of 4 results
Advanced filters: Author: S. Burc Eryilmaz Clear advanced filters

  • Using chips that mimic the human brain to perform cognitive tasks, namely neuromorphic computing, calls for low power and high efficiency hardware. Here, Yaoet al. show on-chip analogue weight storage by integrating non-volatile resistive memory into a CMOS platform and test it in facial recognition.

    • Peng Yao
    • Huaqiang Wu
    • He Qian
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-8

  • A compute-in-memory neural-network inference accelerator based on resistive random-access memory simultaneously improves energy efficiency, flexibility and accuracy compared with existing hardware by co-optimizing across all hierarchies of the design.

    • Weier Wan
    • Rajkumar Kubendran
    • Gert Cauwenberghs
    ResearchOpen Access
    Nature
    Volume: 608, P: 504-512