DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising ma...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) S. 45 - 57
Hauptverfasser:	Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 29.06.2024
Schlagworte:	Accuracy Computational efficiency Computer architecture Dynamical System Graph Learning Graph neural networks Nature-Powered Computing Semiconductor device modeling Transforms Turning
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!