Merge pull request #2700 from zenonxiu81/main

New learn path: Unleashing SME2 Performance - Profile ONNX models with KleidiAI-Optimized ONNX Runtime

Author: jasonrandrews

content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_index.md

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+---
+title: Unleashing SME2 Performance - Profile ONNX models with KleidiAI-Optimized ONNX Runtime
+
+minutes_to_complete: 40
+
+who_is_this_for: This is an advanced topic for software developers, performance engineers, and AI practitioners 
+
+learning_objectives: 
+    - Build ONNX runtime library with KleidiAI and SME2 support
+    - Profile performance of ONNX models
+    - Learn how KleidiAI and SME2 accelerates ONNX operators
+
+prerequisites:
+    - Knowledge of KleidiAI and SME2
+    - An Android device with Arm SME2 support
+
+author: Zenon Zhilong Xiu
+
+### Tags
+skilllevels: Advanced
+subjects: ML
+armips:
+    - Arm C1 CPU
+    - Arm SME2 unit
+tools_software_languages:
+    - C++
+    - ONNX runtime
+operatingsystems:
+    - Android
+    - Linux
+
+
+
+further_reading:
+    - resource:
+        title: part 1 Arm Scalable Matrix Extension Introduction 
+        link: https://developer.arm.com/community/arm-community-blogs/b/architectures-and-processors-blog/posts/arm-scalable-matrix-extension-introduction
+        type: blog
+    - resource:
+        title: part 2 Arm Scalable Matrix Extension Instructions 
+        link: https://developer.arm.com/community/arm-community-blogs/b/architectures-and-processors-blog/posts/arm-scalable-matrix-extension-introduction-p2
+        type: blog
+    - resource:
+        title: part4 Arm SME2 Introduction 
+        link: https://developer.arm.com/community/arm-community-blogs/b/architectures-and-processors-blog/posts/part4-arm-sme2-introduction
+        type: blog
+        
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---

content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/_next-steps.md

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+# ================================================================================
+weight: 21                  # The weight controls the order of the pages. _index.md always has weight 1.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/mobile-graphics-and-gaming/performance_onnxruntime_kleidiai_sme2/build_ort.md

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+---
+title: Build ONNX Runtime with KleidiAI and SME2 for Android
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Build ONNX Runtime and benchmark application with KleidiAI and SME2 support for Android
+
+To run this on an Android device, you must cross-compile ORT using the Android NDK.
+Prerequisites
+-	Android NDK: Version r26b or newer (r27+ recommended for latest SME2 toolchain support).
+-	CMake & Ninja: Ensure these are in your system PATH.
+
+### Build Command
+Run the following from the root of the ONNX Runtime repository:
+```bash
+./build.sh --android     --android_sdk_path $ANDROID_NDK_HOME     --android_ndk_path $ANDROID_NDK_HOME     --android_abi arm64-v8a     --android_api 27    --config RelWithDebInfo --build_shared_lib     --cmake_extra_defines onnxruntime_USE_KLEIDIAI=ON     --cmake_generator Ninja  --parallel 
+```
+
+Note: The flag “onnxruntime_USE_KLEIDIAI=ON” triggers the inclusion of Arm KleidiAI kernels into the MLAS library.
+
+## Profiling Performance with onnxruntime_perf_test
+Once the build is complete, you will find the libonnxruntime.so shared library and onnxruntime_perf_test binary in your build directory. The onnxruntime_perf_test is essential for measuring latency and identifying bottlenecks.
+### Step 1: Push files to Android Device
+```bash
+adb push <build_dir>/Android/RelWithDebInfo/onnxruntime_perf_test /data/local/tmp/
+adb push <build_dir>/Android/RelWithDebInfo/libonnxruntime.so  /data/local/tmp/
+adb push your_model.onnx /data/local/tmp/
+```
+### Step 2: Run the Performance Test
+The perf_test tool allows you to simulate inference and gather statistics. For example,
+```bash
+# Execute on the device
+adb shell "/data/local/tmp/onnxruntime_perf_test -e cpu -m times  -r 20 -s -Z  -x 1 /data/local/tmp/your_model.onnx"
+```
+The command example set the arguments of the application as,
+-	“-e cpu” specifies the provider as cpu provider
+-	“-m times” specifies the test mode as “times”
+-	“-r 20” specifies the repeated times as 20
+-	“-Z” disallows thread from spinning during runs to reduce cpu usage
+-	“-s” shows statistics result
+-	“-x 1” sets the number of threads used to parallelize the execution within nodes as 1
+
+You can try other arguments setting if you would like to.
+
+### Step 3: Deep Dive into Operator Profiling
+To see exactly how many milliseconds are spent on each operator, use the profiling flag -p.
+```bash
+adb shell "/data/local/tmp/onnxruntime_perf_test -p profile.json -e cpu -m times  -r 5 -s -Z  -x 1 /data/local/tmp/your_model.onnx"
+adb pull /data/local/tmp/profile.json
+```
+The argument “-p” enables performance profiling during the benchmark run. When you provide this flag followed by a filename, ONNX Runtime will generate a JSON file containing a detailed trace of the model execution.
+You can view the results by opening [prefetto tool]( https://ui.perfetto.dev/), and loading the generated JSON file. This allows you to see a visual timeline of which operations took the most time.
+You also can convert the JSON file to a CSV sheet by creating a python script.