Merge branch 'main' into streamline

Author: madeline-underwood

.wordlist.txt

@@ -5156,3 +5156,109 @@ transpile
 tsc
 typescriptlang
 vmlinux
+ATfE
+ATfL
+AlmaLinux
+Asher
+AsyncOpenAI
+Bálint
+CVE
+CircleCI's
+Couchbase
+Couchbase's
+DANDROID
+DKLEIDICV
+DataType
+EdgeXpert
+EleutherAI
+Facter
+GDDR
+GEMMs
+GSM
+Gnuplot
+HD
+HellaSwag
+Hiera
+HwCaps
+InceptionV
+Infineon
+Jett
+KRaft's
+Kiro
+KleidiCV's
+LangChain
+LlamaIndex
+MMLU
+MULTIVERSION
+MVT
+Menuconfig
+MobileNetV
+NHWC
+ORM
+Phoronix
+PointwiseConv
+PyBind
+QL
+RecursiveCharacterTextSplitter
+ResNet
+SDOT
+SMES
+SentenceTransformer
+Silabs
+TPUs
+UDOT
+XDCR
+XNNBatchMatrixMultiply
+XNNConv
+XNNFullyConnected
+XnnpackBackend
+Zhou
+acc
+agentless
+aten
+blockwise
+cbc
+couchbase
+ctrl
+datasheets
+decltype
+docx
+etdump
+etrecord
+facter
+faiss
+fg
+fibonacci
+gemm
+hiera
+ipc
+ivh
+js's
+kiro
+kirocli
+libclang
+libopencv
+llamacpp
+llmcompressor
+minmax
+mse
+multiversion
+phoronix
+pillowfight
+pkl
+pointwise
+pqs
+precisions
+proto
+pypdf
+qb
+qc
+qp
+rebalance
+rustup
+sSf
+tcmalloc
+tlsv
+vLLM's
+webp
+HugeTLB

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started.md

@@ -107,7 +107,7 @@ For the compiler, you can use [Clang](https://www.llvm.org/) version 18 or later
 At the time of writing, macOS ships with `clang` version 17.0.0, which doesn't support SME2. Use a newer version, such as 21.1.4, available through Homebrew.
 {{% /notice%}}
 
-You can check your compiler version using the command: `clang --version` if it's alreay installed. If not, install `clang` using the instructions below, selecting either macOS or Linux/Ubuntu, depending on your setup:
+You can check your compiler version using the command: `clang --version` if it's already installed. If not, install `clang` using the instructions below, selecting either macOS or Linux/Ubuntu, depending on your setup:
 
 {{< tabpane code=true >}}
 

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/2-check-your-environment.md

@@ -275,7 +275,7 @@ defined in `misc.c`.
 
 The `sme2_check` program then displays whether SVE, SME and SME2 are supported
 at line 24. The checking of SVE, SME and SME2 is done differently depending on
-`BAREMETAL`. This platform specific behaviour is abstracted by the
+`BAREMETAL`. This platform specific behavior is abstracted by the
 `display_cpu_features()`:
 - In baremetal mode, our program has access to system registers and can inspect system registers for SME2 support. The program will print the SVE field of the `ID_AA64PFR0_EL1` system register and the SME field of the `ID_AA64PFR1_EL1` system register.
 - In non baremetal mode, on an Apple platform the program needs to use a higher

content/learning-paths/embedded-and-microcontrollers/_index.md

@@ -11,7 +11,7 @@ maintopic: true
 operatingsystems_filter:
 - Android: 1
 - Baremetal: 30
-- Linux: 31
+- Linux: 32
 - macOS: 7
 - RTOS: 10
 - Windows: 4
@@ -21,7 +21,7 @@ subjects_filter:
 - Embedded Linux: 4
 - Libraries: 3
 - ML: 17
-- Performance and Architecture: 21
+- Performance and Architecture: 22
 - RTOS Fundamentals: 5
 - Security: 2
 - Virtual Hardware: 2
@@ -35,6 +35,8 @@ tools_software_languages_filter:
 - Arm Compute Library: 2
 - Arm Development Studio: 8
 - Arm Fast Models: 4
+- Arm Performance Studio: 1
+- Arm Streamline: 1
 - Arm Virtual Hardware: 12
 - Assembly: 1
 - C: 5
@@ -72,6 +74,7 @@ tools_software_languages_filter:
 - Keil Studio Cloud: 1
 - Kubernetes: 1
 - lgpio: 1
+- Linux kernel: 1
 - LLM: 2
 - MCP: 1
 - MPS3: 1
@@ -80,6 +83,7 @@ tools_software_languages_filter:
 - NumPy: 1
 - Ollama: 1
 - Paddle: 1
+- Performance analysis: 1
 - Porcupine: 1
 - Python: 8
 - PyTorch: 3

content/learning-paths/embedded-and-microcontrollers/avh_ppocr/end-to-end_workflow.md

@@ -14,7 +14,7 @@ This section provides hands-on instructions for you to deploy pre-trained Paddle
 
 The steps involved in the model deployment are shown in the figure below:
 
-![End-to-end workflow#center](./Figure3.png "Figure 3. End-to-end workflow")
+![End-to-end workflow#center](./figure3.webp "Figure 3. End-to-end workflow")
 
 ## Deploy PaddleOCR text recognition model on the Corstone-300 FVP included with Arm Virtual Hardware
 

content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug.md

@@ -89,4 +89,4 @@ Ensure your FVP instance is running and matches the model and parameters selecte
 
 After these steps, you can debug the software stack as shown in the following figure:
 
-![FVP running #center](Select_target.png "Debug interface in GUI")
+![FVP running #center](select_target.webp "Debug interface in GUI")

content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/run.md

@@ -101,4 +101,4 @@ Always check the name of the CPU instance when switching between different FVP m
 
 You can also run the FVP using its graphical user interface:
 
-![GUI #center](FVP.png "View of the FVP GUI")
+![GUI #center](fvp.webp "View of the FVP GUI")

content/learning-paths/laptops-and-desktops/_index.md

@@ -9,15 +9,15 @@ maintopic: true
 operatingsystems_filter:
 - Android: 2
 - ChromeOS: 2
-- Linux: 35
-- macOS: 9
+- Linux: 36
+- macOS: 10
 - Windows: 46
 subjects_filter:
 - CI-CD: 5
 - Containers and Virtualization: 7
 - Migration to Arm: 30
-- ML: 3
-- Performance and Architecture: 27
+- ML: 4
+- Performance and Architecture: 28
 subtitle: Create and migrate apps for power efficient performance
 title: Laptops and Desktops
 tools_software_languages_filter:
@@ -29,7 +29,7 @@ tools_software_languages_filter:
 - Arm64EC: 1
 - Assembly: 1
 - Bash: 2
-- C: 9
+- C: 10
 - C#: 6
 - C++: 11
 - CCA: 1
@@ -45,14 +45,16 @@ tools_software_languages_filter:
 - GitLab: 1
 - Google Test: 1
 - HTML: 2
+- Hugging Face: 1
 - Hyper-V: 1
 - i3: 1
 - Intrinsics: 1
 - JavaScript: 2
+- KleidiCV: 1
 - Kubernetes: 1
 - KVM: 1
 - Linux: 1
-- llama.cpp: 1
+- llama.cpp: 2
 - LLM: 1
 - LLVM: 2
 - llvm-mca: 1
@@ -65,13 +67,13 @@ tools_software_languages_filter:
 - OpenCV: 1
 - perf: 4
 - PowerShell: 1
-- Python: 7
+- Python: 8
 - QEMU: 1
 - Qt: 2
 - RDP: 1
 - Remote.It: 1
 - RME: 1
-- Runbook: 18
+- Runbook: 17
 - Rust: 2
 - SVE: 1
 - SVE2: 1

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

@@ -10,13 +10,13 @@ key_ip:
 maintopic: true
 operatingsystems_filter:
 - Android: 32
-- Linux: 31
+- Linux: 32
 - macOS: 14
 - Windows: 14
 subjects_filter:
 - Gaming: 6
 - Graphics: 6
-- ML: 13
+- ML: 14
 - Performance and Architecture: 35
 subtitle: Optimize Android apps and build faster games using cutting-edge Arm tech
 title: Mobile, Graphics, and Gaming
@@ -26,32 +26,32 @@ tools_software_languages_filter:
 - Android: 4
 - Android NDK: 2
 - Android SDK: 1
-- Android Studio: 11
+- Android Studio: 12
 - Arm Development Studio: 1
 - Arm Mobile Studio: 1
 - Arm Performance Studio: 3
 - Assembly: 1
 - Bazel: 1
 - C: 4
 - C#: 3
-- C++: 12
+- C++: 13
 - CCA: 1
 - Clang: 12
-- CMake: 1
-- Coding: 1
+- CMake: 2
 - Docker: 1
-- ExecuTorch: 2
+- ExecuTorch: 3
 - Frame Advisor: 1
 - GCC: 12
 - Generative AI: 2
 - Godot: 1
 - Google Pixel 8: 1
 - Google Test: 1
+- Halide: 1
 - Hugging Face: 5
 - Java: 6
 - Jupyter Notebook: 1
-- KleidiAI: 1
-- Kotlin: 7
+- KleidiAI: 2
+- Kotlin: 8
 - LiteRT: 1
 - LLM: 1
 - LLVM: 1
@@ -61,12 +61,12 @@ tools_software_languages_filter:
 - NEON: 1
 - ONNX Runtime: 1
 - OpenGL ES: 1
-- Python: 4
+- Python: 5
 - PyTorch: 2
 - QEMU: 1
 - RenderDoc: 1
 - RME: 1
-- Runbook: 15
+- Runbook: 14
 - Rust: 2
 - SDDiskTool: 1
 - SVE2: 1
@@ -77,6 +77,6 @@ tools_software_languages_filter:
 - Visual Studio Code: 1
 - Vulkan: 5
 - Vulkan SDK: 1
-- XNNPACK: 1
+- XNNPACK: 2
 weight: 3
 ---

content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze_render_graph.md

@@ -19,6 +19,6 @@ Render passes flow from left to right. The render pass that outputs to the swapc
 
 1. In this example, we can see that some render passes have no consumers at all and that they do not contribute to the final rendered output.
 
-    ![Redundant render passes in Frame Advisor's Render Graph alt-text#center](Render_graph_egypt_redundant_rps.png "Figure 4. Redundant render passes")
+    ![Redundant render passes in Frame Advisor's Render Graph alt-text#center](render_graph_egypt_redundant_rps.webp "Figure 4. Redundant render passes")
 
     These render passes could therefore be removed, without affecting the output, saving processing power and bandwidth.