Readers of this blog may be aware that several key industrial players recently released the MX standard for low-precision computation, mainly targeting its use in machine learning. I reviewed the standard in an earlier blog post.
I’m pleased to report that my PhD student Ebby Samson has released an open source RTL hardware implementation of the key operators from the standard. In joint work with Naveen Mellempudi from AMD, Wayne Luk from Imperial and myself, he describes the library in our forthcoming paper at the International Conference on Field-Programmable Logic and Applications. If you will be in Turin in early September, please come and hear Ebby talking about his work.
The library supports all the concrete formats in the standard and more besides. Ebby has also released an extension to the AMD Brevitas quantisation-aware training PyTorch library that lets you train your models with eventual MX implementation in mind.
Please do read our paper, integrate our hardware designs into your work, and use our Brevitas library to do your neural network training! Links to all in the paper.
Thinking 