The link to our GitHub repo in a sibling comment probably does more justice than I could do in an HN comment, but it's essentially an ML-graph-to-machine-code c

The link to our GitHub repo in a sibling comment probably does more justice than I could do in an HN comment, but it's essentially an ML-graph-to-machine-code compiler that focuses on accelerators. The rationale for open-sourcing here, in addition to the general recruiting/hiring benefit, is that we want vendors to target a common interface so that it's easy to make direct comparisons amongst different hardware. I'd say, though, that ML is moving somewhat away from the "graph compiler" approach. PyTorch (and users' experience with TPUs/XLA vs GPUs) has suggested that static graphs aren't desirable for usability or necessary for performance. These days, I'd say write a PyTorch device backend and a fast kernel library.