commit | 637d76697c77abd2c2791a0eb11feab6cfaba995 | [log] [tgz] |
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author | Han-Chung Wang <hanchung@google.com> | Thu Nov 10 02:57:05 2022 +0800 |
committer | GitHub <noreply@github.com> | Wed Nov 09 10:57:05 2022 -0800 |
tree | 9ac8d3fca97dd9652e0703c2a35caed0b7699c7d | |
parent | 59169393837a07155238efbaa274654d187622ed [diff] |
Integrate llvm-project at b048b1b769aa and bump dependencies (#11094) * Reset third_party/llvm-project: b048b1b769aaf93abe22c01ac2ecad7d68762811 (2022-11-08 16:48:26 +0000): [AArch64][SVE2] Add the SVE2.1 pmov instructions * mlir-hlo: 0a819de96902c7a84a1fca6fb33abaded3569b6d * tensorflow: 0eb1ae11dac6c2db6164ca50b80d9584e125b3d1
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.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.