Year: 2024
Type: article
Abstract: In the landscape of modern Fortran programming, there exists a compelling need for neural network libraries tailored to the language.Given the extensive set of legacy codes built with Fortran, there is an ever-growing necessity to provide new libraries implementing on modern data science tools and methodologies.Fortran's inherent compatibility with high-performance computing resources, particularly CPUs, positions it as a language of choice for many machine learning problems.The vast amount of computing capabilities available within current supercomputers worldwide would be an invaluable asset to the growing demand for machine learning and artificial intelligence.The ATHENA library is developed as a resource to bridge this gap; It provides a robust suite of tools designed for building, training, and testing fully-connected and convolutional feed-forward neural networks.
DOI: 10.21105/joss.06492
Author: Ned Thaddeus Taylor
Institution: University of Exeter
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Leverhulme Trust
Award: G4358717740
OpenAlex ID: W4400317145