I wouldn't be worried for MLIR. MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for

I wouldn't be worried for MLIR. MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for example (https://github.com/google/iree https://github.com/google/iree ). The TensorFlow lite converter has been replaced by the new MLIR-based one, similarly for the Edge TPU. But more than that, what makes me confident is the traction we are getting outside Google. First, we landed MLIR in LLVM last month: https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80139a1f2133fad5284691a121 https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80... The LLVM Fortran frontend (f18/flang) which will merge soon in the LLVM monorepo is using MLIR for their own IR. It'll be exiting to develop a non-ML MLIR-based frontend within LLVM! In particular Flang is opening an HPC perspective that could be leveraged by other DSLs later. They are adding an OpenMP dialect to MLIR right now: https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/397 https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/39... Intel has been actively porting their nGraph/PlaidML framework to be based on MLIR (search for "The Stripe dialect" and "nGraph Dialect" here: https://mlir.llvm.org/talks/ https://mlir.llvm.org/talks/ ). I'm less familiar with S4TF, but I know they recently got some nice new hires, including https://twitter.com/DaveAbrahams/status/1207690883782467584 https://twitter.com/DaveAbrahams/status/1207690883782467584