| commit | a78cee1f0e84e99eaca8b0ae46e2da609916c6fb | [log] [tgz] |
|---|---|---|
| author | Phoebe Chen <chen.miat@gmail.com> | Tue May 14 23:04:36 2024 +0800 |
| committer | GitHub <noreply@github.com> | Tue May 14 08:04:36 2024 -0700 |
| tree | 3bdd9d512eab36f85e7debb0f8e9856e5e7d216b | |
| parent | 8d8d18c50600462136e9136927384e368672079d [diff] |
Add support for serializing the textual representation of LLVM IR. (#17193) This PR adds support for direct serialization of LLVM IR textual representation from the module, eliminating the need for developers to use the llvm-dis tool to convert bitcode into human-readable IR format. This enhancement simplifies debugging and optimization processes, providing developers with a more streamlined workflow. --------- Signed-off-by: Phoebe Chen <phoebe.chen@sifive.com>
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.