commit | 00a3f2c1a46ff9167c567ee3466baaf8c8a0d881 | [log] [tgz] |
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author | Han-Chung Wang <hanchung@google.com> | Thu Mar 10 09:53:07 2022 -0800 |
committer | GitHub <noreply@github.com> | Thu Mar 10 09:53:07 2022 -0800 |
tree | cd3c0063740b2fbffaf455d5c543995288e225a4 | |
parent | 1407c5e76639c90c68b9df4ca9b9a5d9d53bc17f [diff] |
Heuristically pick up tiling sizes to vectorize Linalg operations. (#8517) Configurations: taskset 80 + dylib-sync on local Pixel 4 | Model | Before | After | | ---------------- | ------- | ------- | | DeepLabV3 | 711 ms | 188 ms | | MobileBertSquad | 672 ms | 673 ms | | MobileNetV2 | 103 ms | 46.4 ms | | MobileNetV3Small | 24.6 ms | 14.6 ms | | MobileSSD | 204 ms | 166 ms | | PoseNet | 545 ms | 241 ms | Configurations: taskset f0 + dylib on local Pixel 4 | Model | Before | After | | ---------------- | ------- | ------- | | DeepLabV3 | 358 ms | 161 ms | | MobileBertSquad | 376 ms | 376 ms | | MobileNetV2 | 65.6 ms | 29.8 ms | | MobileNetV3Small | 20.7 ms | 15.7 ms | | MobileSSD | 94 ms | 79.9 ms | | PoseNet | 183 ms | 121 ms |
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.