From 88b006bce9fa2158cfae2bf91fcf45d9903b4b40 Mon Sep 17 00:00:00 2001
From: Anton Voronov <anton.voronov@intel.com>
Date: Thu, 1 Jun 2023 12:17:48 +0400
Subject: [PATCH] [DOC] cpu documentation fixes (#17816)

* [DOC] cpu documentation fixes

* fixed typos
---
 docs/OV_Runtime_UG/supported_plugins/CPU.md | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/docs/OV_Runtime_UG/supported_plugins/CPU.md b/docs/OV_Runtime_UG/supported_plugins/CPU.md
index 41b753fe387..1fb3daf4d76 100644
--- a/docs/OV_Runtime_UG/supported_plugins/CPU.md
+++ b/docs/OV_Runtime_UG/supported_plugins/CPU.md
@@ -9,7 +9,7 @@
 The CPU plugin is a part of the Intel® Distribution of OpenVINO™ toolkit. It is developed to achieve high performance inference of neural networks on Intel® x86-64 and Arm® CPUs. The newer 11th generation and later Intel® CPUs provide even further performance boost, especially with INT8 models.
 For an in-depth description of CPU plugin, see:
 
-- `CPU plugin developers documentation <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/cmake_options_for_custom_comiplation.md>`__.
+- `CPU plugin developers documentation <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/cmake_options_for_custom_compilation.md>`__.
 - `OpenVINO Runtime CPU plugin source files <https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_cpu/>`__.
 
 .. note::
@@ -94,8 +94,7 @@ the `BFLOAT16 – Hardware Numerics Definition white paper <https://software.int
 
 Using the ``bf16`` precision provides the following performance benefits:
 
-- ``bfloat16`` data type allows using Intel® Advanced Matrix Extension (AMX), which provides dramatically faster computations on corresponding hardware in
-comparison with AVX512 or AVX2 instructions in many DL operation implementations.
+- ``bfloat16`` data type allows using Intel® Advanced Matrix Extension (AMX), which provides dramatically faster computations on corresponding hardware in comparison with AVX512 or AVX2 instructions in many DL operation implementations.
 - Reduced memory consumption since ``bfloat16`` data half the size of 32-bit float.
 
 To check if the CPU device can support the ``bfloat16`` data type, use the :doc:`query device properties interface <openvino_docs_OV_UG_query_api>` 
@@ -296,7 +295,7 @@ For more details, see the :doc:`model caching <openvino_docs_OV_UG_Model_caching
 Extensibility
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-CPU plugin supports fallback on ``ov::Op`` reference implementation if the plugin do not have its own implementation for such operation.
+CPU plugin supports fallback on ``ov::Op`` reference implementation if the plugin does not have its own implementation for such operation.
 That means that :doc:`OpenVINO™ Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>` can be used for the plugin extension as well.
 Enabling fallback on a custom operation implementation is possible by overriding the ``ov::Op::evaluate`` method in the derived operation 
 class (see :doc:`custom OpenVINO™ operations <openvino_docs_Extensibility_UG_add_openvino_ops>` for details).
@@ -399,7 +398,7 @@ By default, OpenVINO Runtime will enable CPU threads pinning for better performa
       :language: python
       :fragment: [ov:intel_cpu:multi_threading:part1]
 
-user can check [optimization guide](https://docs.openvino.ai/latest/openvino_docs_deployment_optimization_guide_tput_advanced.html) for details on multi-stream execution
+user can check the :doc:`optimization guide <openvino_docs_deployment_optimization_guide_tput_advanced>` for details on multi-stream execution
 
 .. note:: 
 
@@ -463,7 +462,7 @@ weights are loaded from DDR/L3 cache in the packed format this significantly dec
 and as a consequence improve inference performance.
 
 To use this feature, the user is provided with property ``sparse_weights_decompression_rate``, which can take 
-values from the interval \[0, 1\]. ``sparse_weights_decompression_rate`` defines sparse rate threashold: only operations 
+values from the interval \[0, 1\]. ``sparse_weights_decompression_rate`` defines sparse rate threshold: only operations 
 with higher sparse rate will be executed using ``sparse weights decompression feature``. The default value is ``1``, 
 which means the option is disabled.