commit | 5392b83b7121dad378298c1ad2dbbcc61de484b4 | [log] [tgz] |
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author | Ben Vanik <ben.vanik@gmail.com> | Mon May 13 09:07:44 2024 -0700 |
committer | Ben Vanik <ben.vanik@gmail.com> | Mon Jul 29 20:32:22 2024 -0700 |
tree | ba1bc39844c6eb71c5f274aa80750fd732499a12 | |
parent | 31074e728b180234272204a90a7adec4abd0696d [diff] |
Adding flow.tensor.transfer op. This allows for frontends to specify a clone of a tensor to a target context. This is lowered into a stream.async.transfer and with analysis will allow for hinting placement. More flow-level optimizations are likely to be required in larger programs but until we start to see those things are kept simple here.
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.