We propose to create SIG OpenXLA to facilitate development of an open, state-of-art ML compiler, built collaboratively with ML hardware & framework developers, using the best of XLA & MLIR.

## Objective

OpenXLA will be a community-driven and modular open source compiler. It will enable efficient lowering, optimization and deployment of ML models from most major frameworks to any hardware backend notably CPUs, GPUs, and ML ASICs. This work will be done collaboratively with major ML frameworks and hardware vendors.

SIG OpenXLA will focus on creating the OpenXLA project, including the extraction of XLA from TensorFlow into a standalone project. SIG discussions will facilitate coordination around roadmap, design evolution, and new workflows to be created in OpenXLA. 

## Goals

* Accelerate industry collaboration around XLA and build a vibrant OSS community.
* Share and receive feedback on the technical direction for OpenXLA and ensure it meets the needs of major users and contributors.
* Set up a new XLA repository or organization with independent build/test, with infra to more easily accept PRs, and that is hardware and framework independent. 
* Ensure the extraction of XLA from TensorFlow is minimally disruptive to existing users and contributors.  
* Create a product identity with its own brand, website, docs, and communication channels.
* Discuss establishment of governance outside TensorFlow.