DS-TPU: Dynamical System for on-Device Lifelong Graph Learning with Nonlinear Node Interaction

作者：Chunshu Wu, Ruibing Song, Chuan Liu, Pouya Haghi, Ang Li, Michael Huang, Tong Geng · 年份：2025 · DOI：10.1145/3695053.3731091 · 被引用次数：3 · 研究领域：Advanced Graph Neural Networks、Advanced Bandit Algorithms Research、Data Stream Mining Techniques

Graph learning on dynamical systems has recently surfaced as an emerging research domain.By leveraging a novel electronic Dynamical System (DS), various graph learning challenges have been effectively tackled through a rapid, spontaneous natural annealing process.This method has attracted increasing attention due to its orders-of-magnitude improvements in speed and energy efficiency compared to traditional Graph Neural Network (GNN) approaches for inference tasks.However, (1) the current DS hardware only supports inference, missing its native solution for training; while relying on conventional hardware is likely more expensive than GNNs.(2) The current DS architecture only allows linear interactions among its nodes, limiting training accuracy.In this work, we present a Dynamical-System Training-Processing Unit (DS-TPU) developed through algorithm-architecture co-design to tackle the two major challenges: (1) An on-device lifelong learning mechanism that leverages feedback electric current as the loss function in response to the observed training data, allowing electron-speed refinement on the present model parameters.(2) A nonlinear DS node interaction mechanism constructed from Chebyshev polynomials to significantly improve the compatibility between the DS hardware and the embedded relation of graph data.Extensive evaluations using six real-world graph learning applications demonstrate that for accuracy, DS-TPU achieves 10.8% MAE reduction over the best results of five wide...