A sub-10ms neural dynamical system based on phase change memristors

A sub-10ms neural dynamical system based on phase change memristors Introduction High-fidelity geometry under strict topological constraints remains a critical challenge for realizing physical-world models, particularly for accurate genus-0 homeomorphic deformations that require real-time, dense, and smoothly differentiable deformation fields on manifolds. Neural dynamical systems (NDS) excel at these tasks using adaptive-stepsize numerical integration with embedded neural networks (ENNs), but causing heavy data movements and iterative stepsize searches. As a result, the NDS runtime remains on the order of hundreds of milliseconds with classical ENNs based on multi-layer perceptrons, limiting practical deployments. Here we report a low-latency NDS of sub-10ms leveraging two key attributes of phase-change memristors (PCM): conductance drift and multilevel conductance.基于相变忆阻器（phase change memristors）的亚10毫秒神经动力学系统 引言 严格拓扑约束下的高保真几何建模仍是物理世界模型实现的关键挑战，尤其针对需在流形上实现实时、稠密且平滑可微形变场的精确亏格0同胚形变任务。神经动力学系统（Neural Dynamical Systems, NDS）凭借搭载嵌入神经网络（Embedded Neural Networks, ENNs）的自适应步长数值积分方法在这类任务中表现优异，但会产生大量数据移动与迭代步长搜索操作。因此，基于多层感知器（Multi-Layer Perceptrons, MLP）的经典嵌入神经网络所构建的神经动力学系统，其运行时延仍处于数百毫秒量级，限制了其实际部署应用。本文报道了一种低延迟亚10毫秒神经动力学系统，其利用了相变忆阻器（phase change memristors, PCM）的两项关键特性：电导漂移与多电平电导。

搜集汇总

背景与挑战

背景概述

该数据集提供了一个基于相变忆阻器的神经动力学系统，其关键特点是运行时间低于10毫秒，解决了传统神经动力学系统因数据移动和迭代步长搜索导致的数百毫秒延迟问题。数据集包含实验数据文件，大小为5.3 MB，发布于2026年，适用于低延迟物理世界模型和实时变形场研究。