commit | b49468ddf11a1103d82f11009a0b3253a49705aa | [log] [tgz] |
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author | Luke Hutton <luke.hutton@arm.com> | Wed May 15 11:28:16 2024 +0100 |
committer | GitHub <noreply@github.com> | Wed May 15 11:28:16 2024 +0100 |
tree | 09c34bf82638f5f58f5fee6cf9f0fc1a1496554a | |
parent | cfe1711934f82e56f147f2f5f9f928b5a9b92b3e [diff] |
[SME] Introduce scalable fp32 dense schedule (#16921) This commit adds a new scalable fp32 dense schedule that calls SME intrinsics according to the SME RFC: https://github.com/apache/tvm-rfcs/pull/107. Currently the schedule does not make use of predication, meaning the output from the matmul compute must be copied in a subsequent compute stage. This will be removed once support for predication is added.
Documentation | Contributors | Community | Release Notes
Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.
TVM is licensed under the Apache-2.0 license.
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.
We learned a lot from the following projects when building TVM.