Update Intel GPU resources references (#27994)

# PR Summary
Small PR - Commits 1e878b6 and
c6ec6d4 moved bunch of Intel GPU
resources. This PR adjusts sources to changes.

Signed-off-by: Emmanuel Ferdman <[email protected]>

src/plugins/intel_gpu/docs/gpu_debug_utils.md

@@ -268,7 +268,7 @@ Each file contains a single buffer in a common planar format (`bfyx`, `bfzyx`, o
 shape: [b:1, f:1280, x:1, y:1, z:1, w:1, g:1] (count: 1280, original format: b_fs_yx_fsv16)
 ```
 
-For troubleshooting the accuracy, you may want to compare the results of GPU plugin and CPU plugin. For CPU dump, see [Blob dumping](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_cpu/src/docs/blob_dumping.md)
+For troubleshooting the accuracy, you may want to compare the results of GPU plugin and CPU plugin. For CPU dump, see [Blob dumping](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_cpu/docs/debug_capabilities/blob_dumping.md)
 
 
 ## Run int8 model on Gen9 HW

src/plugins/intel_gpu/docs/gpu_plugin_unit_test.md

@@ -23,8 +23,8 @@ openvino/src/plugins/intel_gpu/tests	- root of Intel GPU unit test
   - Fusion is an algorithm that fuses several operations into one optimized operation. For example, two nodes of `conv -> relu` may be fused into a single node of `conv`.
   - Fusion unit tests checks whether the fusion is done as expected.
   - fusion_test_common.cpp
-     - The base class for a fusing test, that is, [BaseFusingTest](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/fusions/fusion_test_common.hpp#L19), is implemented here. It tests whether the fusing is successful or not by comparing the execution results of the two networks, one is the fused network, the other is non-fused network for the same topology.
-       - [BaseFusingTest](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/fusions/fusion_test_common.hpp#L19) has an important method called `compare()`.
+     - The base class for a fusing test, that is, [BaseFusingTest](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp#L20), is implemented here. It tests whether the fusing is successful or not by comparing the execution results of the two networks, one is the fused network, the other is non-fused network for the same topology.
+       - [BaseFusingTest](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp#L20) has an important method called `compare()`.
        - `compare()` method has the following three tasks:
             - Execute two networks (fused network and not fused network)
             - Compare the actual number of executed primitives with the expected number of executed primitives in test params
@@ -138,9 +138,9 @@ GPU unit tests are using two types of test macros (**TEST** and **TEST_P**)  in
 - **TEST** checks the test result by comparing the execution results with expected values after running network created from the target topology to check.
   - It is important to generate test input and expected output result in **TEST**
   - You can create input data and expected output data using these three approaches:
-    - Generate simple input data and calculate the expected output data from input data manually, like [basic_deformable_convolution_def_group1_2](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/test_cases/convolution_gpu_test.cpp#L254)
-    - Generate random input and get the expected output, using reference function, which is made in the test codes like [mvn_test_across_channels_outside_sqrt_bfyx](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/test_cases/mvn_gpu_test.cpp#L108)
-    - Generate random input and get the expected output from another reference kernel which exists in clDNN kernels like [mvn_random_test_bsv32](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/test_cases/mvn_gpu_test.cpp#L793)
+    - Generate simple input data and calculate the expected output data from input data manually, like [basic_deformable_convolution_def_group1_2](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp#L224)
+    - Generate random input and get the expected output, using reference function, which is made in the test codes like [mvn_test_across_channels_outside_sqrt_bfyx](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/unit/test_cases/mvn_gpu_test.cpp#L138)
+    - Generate random input and get the expected output from another reference kernel which exists in clDNN kernels like [mvn_random_test_bsv32](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/unit/test_cases/mvn_gpu_test.cpp#L762)
 
 - When you allocate input data, keep in mind that the layout order in `engine.allocation_memory` is not `bfyx` but `bfxy`. For example, if input is `{1,1,4,5}`, the layout should be as below:
 
@@ -151,7 +151,7 @@ GPU unit tests are using two types of test macros (**TEST** and **TEST_P**)  in
 ## fusions
 
 - It is implemented based on **TEST_P** because there are many cases where multiple layouts are tested in the same topology.
-- If the fusing test class already exists, you can use it. Otherwise, you should make a new fusing test class, which is inherited [BaseFusingTest](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/fusions/fusion_test_common.hpp#L19).
+- If the fusing test class already exists, you can use it. Otherwise, you should make a new fusing test class, which is inherited [BaseFusingTest](https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp#L20).
   - The new fusing test class should create the `execute()` method, which creates fused / non-fused networks and calls `compare` method after setting input.
 - Create a test case, using **TEST_P**:
   - You can make the desired networks using create_topologies.