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AI CURATED HOT STORY · 11/3/2026

Peking U and IBM Define Modern Analog Computing for Matrix Acceleration

1 reports archived1 independent sourcesupdated 11/3/2026, 11:16:23
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Researchers from Peking University and IBM Zurich have published a review defining 'Modern Analog Computing,' shifting focus from differential equations to AI-centric matrix operations. Leveraging resistive memory (RRAM) for high precision and scalability, this framework aims to address energy bottlenecks in large models and establish new industry standards.

LATEST/Researchers from Peking University and IBM Zurich have published a review defining 'Modern Analog Computing,' shifting focus from differential equations to AI-centric matrix operations. Leveraging resistive memory (RRAM) for high precision and scalability, this framework aims to address energy bottlenecks in large models and establish new industry standards.

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  1. 量子位官网T2·78 pts

    Peking U and IBM Define Modern Analog Computing for Matrix Acceleration

    • Formally distinguishes 'traditional' from 'modern' analog computing, shifting core tasks from differential equations to AI matrix operations
    • Hardware foundation moves from CMOS circuits to Resistive RAM (RRAM), emphasizing high precision and engineering scalability
    • Jointly endorsed by Peking University, IBM, and Silicon Valley startups to unify industry standards and drive commercialization