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 ...
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.
