Neural Acceleration of Graph Based Utility Functions for Sparse Matrices

Neural Acceleration of Graph Based Utility Functions for Sparse Matrices

Many graph-based algorithms in high performance computing (HPC) use approximate solutions due to having algorithms that are computationally expensive or serial in nature. Neural acceleration, i.e., the process of speeding up approximation computation elements via artificial neural networks, is relat...

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Bibliographic Details
Main Authors: Joshua Dennis Booth, Gregory S. Bolet
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Approximate computing
high performance computing
linear algebra
sparse matrices
machine learning algorithms
neural networks
Online Access:https://ieeexplore.ieee.org/document/10082918/

Internet

https://ieeexplore.ieee.org/document/10082918/

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