NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator

Graph Neural Networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatics. Despite their effectiveness, their adoption has not been pervasive because of scalability challenges associated with large-...

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Bibliographic Details

Published in:	2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) pp. 946 - 960
Main Authors:	Shivdikar, Kaustubh, Agostini, Nicolas Bohm, Jayaweera, Malith, Jonatan, Gilbert, Abellan, Jose L., Joshi, Ajay, Kim, John, Kaeli, David
Format:	Conference Proceeding
Language:	English
Published:	IEEE 29.06.2024
Subjects:	Computer architecture Decoupled Computations Graph neural networks Graph Neural Networks (GNN) Hardware-software co-design On-chip Memory Organizations Prefetching Scalability Social networking (online) Source coding Sparse Matrix Multiplication (SpGEMM) Spatial Accelerators
Online Access:	Get full text
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