commit | 99159fa149ef07f54d6a248120934dcb8b7b4f8d | [log] [tgz] |
---|---|---|
author | Han-Chung Wang <hanchung@google.com> | Wed Nov 09 09:59:51 2022 +0800 |
committer | GitHub <noreply@github.com> | Wed Nov 09 01:59:51 2022 +0000 |
tree | 509127dcefdfc52f83456ea89a5af7e875ddafa2 | |
parent | c074522d0c2f0cc5c798f9f23c85f7314e799710 [diff] |
Integrate llvm-project at 6c6dff7e2c27 and bump dependencies (#11069) * Reset third_party/llvm-project: 6c6dff7e2c27c5a9ea9466d49f61a1edc82bc364 (2022-11-04 23:20:37 -0400): [libc] Add add_with_carry to builtin wrapper. * mlir-hlo: 4cb08f5b1d612c2d9bc6011aaf73bd0141b7e5fa * tensorflow: cdb73b19739ccd72fcb659d220bf71924c056ced Cherry-pick https://github.com/llvm/llvm-project/commit/7c2d3153a9481793da58894dbf35d4994f3b67a4
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.