Merge pull request #2590 from jasonrandrews/review

spelling, links, and tags

Author: jasonrandrews

.wordlist.txt

@@ -5156,3 +5156,108 @@ 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

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/embedded-and-microcontrollers/streamline-kernel-module/3_oot_module.md

@@ -223,7 +223,7 @@ The module above receives the size of a 2D array as a string through the `char_d
     ssh root@<your-target-ip>
     ```    
 
-4. Execute the following commads on the target to run the module:
+4. Execute the following commands on the target to run the module:
     ```bash
     insmod /root/mychardrv.ko
     mknod /dev/mychardrv c 42 0

content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/4_sl_profile_oot.md

@@ -70,7 +70,7 @@ If you are using an AArch32 target, use `arm` instead of `arm64`.
 
     ![Streamline command#center](./images/img04_streamline_cmd.png)
 
-8. In the Capture settings dialog, select Add image, add the absolut path of your kernel module file `mychardrv.ko` and click Save.
+8. In the Capture settings dialog, select Add image, add the absolute path of your kernel module file `mychardrv.ko` and click Save.
 ![Capture settings#center](./images/img05_capture_settings.png)
 
 9. Start the capture and enter a name and location for the capture file. Streamline will start collecting data and the charts will show activity being captured from the target.

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