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Showing 1–6 of 6 results
Advanced filters: Author: Ambrogio Fasoli Clear advanced filters

  • The authors report the implementation of a Transformer-based model on the same architecture used in Large Language Models in a 14nm analog AI accelerator with 35 million Phase Change Memory devices, which achieves near iso-accuracy despite hardware imperfections and noise.

    • An Chen
    • Stefano Ambrogio
    • Geoffrey W. Burr
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-11

  • Efforts to demonstrate the feasibility of fusion power can benefit from studies of fundamental questions in plasma physics carried out in simplified devices.

    • Ambrogio Fasoli
    • Ivo Furno
    • Paolo Ricci
    Comments & Opinion
    Nature Physics
    Volume: 15, P: 872-875

  • Device-level complexity represents a big shortcoming for the hardware realization of analogue memory-based deep neural networks. Mackin et al. report a generalized computational framework, translating software-trained weights into analogue hardware weights, to minimise inference accuracy degradation.

    • Charles Mackin
    • Malte J. Rasch
    • Geoffrey W. Burr
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12

  • A newly designed control architecture uses deep reinforcement learning to learn to command the coils of a tokamak, and successfully stabilizes a wide variety of fusion plasma configurations.

    • Jonas Degrave
    • Federico Felici
    • Martin Riedmiller
    ResearchOpen Access
    Nature
    Volume: 602, P: 414-419

  • Simulating magnetically confined fusion plasmas is crucial to understand and control them. Here, the state of the art and the multi-physics involved are discussed: electromagnetism and hydrodynamics combined over vast spatiotemporal ranges.

    • A. Fasoli
    • S. Brunner
    • L. Villard
    Reviews
    Nature Physics
    Volume: 12, P: 411-423