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Update LICENSE, TPN and description for v5.0
- Update the description in readme.md file. - Update the LICENSE and TPN. Signed-off-by: SAVAN ANADANI <Savan.Anadani@amd.com> Change-Id: Icd28cc72c5cb1fc19a2e5c785ef9edbd3b634a3a Signed-off-by: SAVAN ANADANI <Savan.Anadani@amd.com>
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LICENSE

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LICENSE for ZenDNN v5.0 Beta + Tensorflow v2.17.0 Source - AMD copyrighted code
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LICENSE for ZenDNN v5.0 + Tensorflow Source - AMD copyrighted code
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README.md

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# TensorFlow-ZenDNN Plug-in For AMD CPUs
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**EARLY ACCESS:** The ZenDNN TensorFlow* Plugin (zenTF) extends TensorFlow* with an innovative upgrade that's set to revolutionize performance on AMD hardware.
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**The latest ZenDNN Plugin for TensorFlow*** **(zentf) 5.0 is here!**
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As of version 5.0, AMD is unveiling a game-changing upgrade to ZenDNN, introducing a cutting-edge plug-in mechanism and an enhanced architecture under the hood. This isn't just about extensions; ZenDNN's aggressive AMD-specific optimizations operate at every level. It delves into comprehensive graph optimizations, including pattern identification, graph reordering, and seeking opportunities for graph fusions. At the operator level, ZenDNN boasts enhancements with microkernels, mempool optimizations, and efficient multi-threading on the large number of AMD EPYC cores. Microkernel optimizations further exploit all possible low-level math libraries, including [AOCL BLIS](https://www.amd.com/en/developer/aocl/blis.html).
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This powerful upgrade continues to redefine deep learning performance on AMD EPYC™ CPUs, combining relentless optimization, innovative features, and industry-leading support for modern workloads.
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The result? Enhanced performance with respect to baseline TensorFlow*. The ZenDNN TensorFlow* Plugin is compatible with TensorFlow versions 2.16 and later.
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zentf 5.0 includes enhancements for bfloat16 performance, primarily by leveraging microkernels and operators from the ZenDNN 5.0 library. These operators are designed to better leverage the EPYC microarchitecture and cache hierarchy.
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The zentf 5.0 plugin works seamlessly with TensorFlow versions 2.17 and 2.16, offering a high-performance experience for deep learning on AMD EPYC™ platforms.
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## Support
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Please note that zenTF is currently in “Early Access” mode. We welcome feedback, suggestions, and bug reports. Should you have any of these, please contact us on zendnn.maintainers@amd.com
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We welcome feedback, suggestions, and bug reports. Should you have any of the these, please kindly file an issue on the ZenDNN Plugin for TensorFlow Github page: https://github.com/amd/ZenDNN-tensorflow-plugin/issues
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## License
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THIRD-PARTY-NOTICES

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Third Party Notices for TF v2.17.0 + ZenDNN v5.0 Beta
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Third Party Notices for ZenDNN v5.0 + Tensorflow
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This summary and the license information provided below is for reference purposes and is not intended to be a comprehensive list of all copyright notices or license terms and conditions applicable to the Software.
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With respect to any license that requires AMD to make available to recipients of object code distributed by AMD pursuant to such license the corresponding source code, and if you desire to receive such source code from AMD, then AMD hereby offers (which offer is valid for as long as required by the applicable license; and we may charge you the cost thereof unless prohibited by the license) to provide you with a copy of such source code; and to accept such offer send a letter requesting such source code (please be specific by identifying the particular Software you are inquiring about (name and version number), to: Advanced Micro Devices, Inc., Legal Department, Attention: Software Compliance Officer, 2485 Augustine Drive, Santa Clara, CA U.S.A. 95054.
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abseil-abseil-cpp v-u (Apache-2.0)
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Copyright Statements
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Copyright 2018 The Abseil Authors.
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See the License for the specific language governing permissions and limitations under the License.
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https://spdx.org/licenses/Apache-2.0.html
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AMD copyrighted code (Apache-2.0)
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Copyright Statements
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* Copyright (c) 2021 Advanced Micro Devices, Inc. All rights reserved.
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License Text https://spdx.org/licenses/Apache-2.0.html
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Apache-2.0
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http://www.apache.org/licenses/
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