SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence. Efficiently adapting SpMV algorithms to diverse matrices and architectures requires a framework capable of accurately recognizing sparse patterns and selecting the optimal...

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Bibliographic Details

Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Lin, Shengle, Liu, Chubo, Ding, Yan, Zhou, Joey Tianyi, Li, Kenli, Yang, Wangdong
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Accuracy Libraries Machine learning Machine learning algorithms Neural networks Pattern recognition Prediction algorithms Predictive models Scientific computing Sparse matrices Sparse Matrix SpMV
Online Access:	Get full text
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