2026-07-20 北京大学（PKU）
Fig. 1. Overview of NDS hardware with multilevel and fine-grained CCD memristor.
＜関連情報＞
- https://newsen.pku.edu.cn/news_events/news/research/15620.html
- https://www.science.org/doi/10.1126/science.aee6277
相変化メモリスに基づく10ミリ秒未満のニューラルダイナミクスシステム A sub–10-millisecond neural dynamical system based on phase-change memristors
Lei Cai, Yaoyu Tao, Chenchen Xie, Longhao Yan, […] , and Yuchao Yang
Science Published:2 Jul 2026
DOI:https://doi.org/10.1126/science.aee6277
Abstract
High-fidelity geometry for physical-world modeling demands real-time, dense, and differentiable deformation fields on manifolds. Neural dynamical systems (NDSs) using adaptive stepsize integration with embedded neural networks excel at these tasks but still suffer latency on the order of hundreds of milliseconds. In this work, we report a sub–10-millisecond NDS hardware leveraging the precisely controlled conductance drift of phase-change memristors and their multilevel compute-in-memory capabilities. We fabricated a 40-nanometer NDS chip for the challenging surface reconstruction tasks. Compared with state-of-the-art NDS hardware, our NDS design achieves a latency of 2.12 milliseconds (below 10 milliseconds) for single-iteration NDS computations with an error tolerance of 10−7 and delivers 3.82× to 36.27× faster speed while consuming 11.75× to 24.73× less power. The end-to-end NDS latency through hardware measurements and simulations outperformed graphics processing unit A100 by 50.38× to 478.18×.
