Great work. It's a real bowl of fresh air coming from huge framework that use cuda. So many different cuda version, with each framework using its own, that al

Great work. It's a real bowl of fresh air coming from huge framework that use cuda. So many different cuda version, with each framework using its own, that all rely on a different driver, and everything needs a new version every 3 months and takes ~10G, (and don't even talk about cudnn needing some manual logged-in install). Here everything is just two files. For embedded system that don't have a GPU it's perfect. Here the parallelization and vectorization has been done by hand, but there is a glimmer of hope coming from the side of various compiler projects : Here is an interesting intel project that does the parallelization and vectorization automatically for different architecture that's definitely worth a look : https://ispc.github.io/ispc.html https://ispc.github.io/ispc.html For the auto-differentiation when I need performance or memory, I currently use tapenade ( http://tapenade.inria.fr:8080/tapenade/index.jsp http://tapenade.inria.fr:8080/tapenade/index.jsp ) and/or manually written gradient when I need to fuse some kernel, but Enzyme ( https://enzyme.mit.edu/ https://enzyme.mit.edu/ ) is also very promising. MPI for parallelization across machines.