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...

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Vydané v:	2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) s. 45 - 57
Hlavní autori:	Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 29.06.2024
Predmet:	Accuracy Computational efficiency Computer architecture Dynamical System Graph Learning Graph neural networks Nature-Powered Computing Semiconductor device modeling Transforms Turning
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