commit | b1cc1a7ab46b61d5c3114aeba73720c4cc04dd7a | [log] [tgz] |
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author | bjacob <benoitjacob@google.com> | Mon Sep 26 21:06:53 2022 +0000 |
committer | GitHub <noreply@github.com> | Mon Sep 26 21:06:53 2022 +0000 |
tree | 9e4927958f1e046f90f967423187d8d41d2302a4 | |
parent | 184221098a0ae7f8087497b9d2f0bfd2b0b90d12 [diff] |
simple streaming kernel for the i8mm case (#10552) @silvasean you were right, the "max streaming" idea from ruy kernels still helps, even on big OOO cores that "should" be able to make that optimization by themselves. This is a 2x partial unrolling + pipelining of the inner loop that allows using all of the available registers to maximize load-to-use distance. It might be counterproductive on cores that care foremost about minimum loop code size such as the Cortex-X1, but I don't have one here to test. Will be a good use case for tuning. On the 3 core types in the Snapdragon 8gen1 in my Moto Edge x30: | Gop/s before | Gop/s after | Gop/s peak | Speedup | %peak before | %peak after -- | -- | -- | -- | -- | -- | -- Cortex-A510 | 83.4 | 103.0 | 114.24 | 1.24 | 73% | 90% Cortex-A710 | 307.7 | 308.0 | 319.49 | 1.00 | 96% | 96% Cortex-X2 | 675.4 | 732.3 | 766.77 | 1.08 | 88% | 96%
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.