spelling and tags

Author: jasonrandrews

.wordlist.txt

@@ -5288,4 +5288,99 @@ menuconfig
 oss
 saas
 todo
-yq
+yq
+AIoT
+AQ
+BSPs
+BatchMatMul
+Bitnami
+CIX
+Deconvolution
+EdgeBloXagent
+FFI
+FLOPs
+FtVjdW
+FullyConnected
+Guo
+HgJyHlhCVQrqvpGdCcaf
+InfluxDB
+IntelliJ
+IqNP
+JetBrains
+Jiaming
+Jinja
+LiteRT's
+MPAC
+MergeTree
+MicroPac
+MicroPacFile
+MicroPacs
+MicroStack
+Micropac
+NeuroStack
+NodeSource
+Podman
+PyCharm
+QD
+RNiiiIPCSUA
+RabbitMQ
+Radxa
+Regardles
+Slurm
+StrongPassword
+TCO
+TinkerBlox
+Tinkerblox
+TransposeConv
+UltraEdge
+Ultraedge
+Uncloud
+WebStorm
+WhatsApp
+XNNPACK's
+applicationType
+bded
+binfmt
+binfmts
+buildSteps
+centre
+createdBy
+dFamiliarity
+df
+django
+ernie
+ffn
+fungibility
+gemini
+getstarted
+ize
+jinja
+mcpserver
+microboost
+micropac
+mlBZgDFc
+moe
+mpac
+neuroboost
+pathbreaking
+pika
+qyM
+rabbitmq
+rabbitmqadmin
+reachability
+reflash
+relevent
+slurm
+sr
+subtasks
+sv
+sysreport
+tA
+templated
+tinkerblox
+tinkerbloxdev
+ultraedge
+unclound
+wNdi
+whatsapp
+workdir

content/learning-paths/automotive/_index.md

@@ -11,12 +11,13 @@ subtitle: Build secure, connected, smart IoT devices
 title: Automotive
 weight: 4
 subjects_filter:
-- Containers and Virtualization: 3
+- Containers and Virtualization: 4
 - Performance and Architecture: 6
 operatingsystems_filter:
 - Baremetal: 1
-- Linux: 8
+- Linux: 9
 - macOS: 1
+- other: 1
 - RTOS: 1
 tools_software_languages_filter:
 - Arm Development Studio: 1
@@ -33,6 +34,7 @@ tools_software_languages_filter:
 - Raspberry Pi: 1
 - ROS 2: 3
 - Rust: 1
+- Tinkerblox: 1
 - topdown-tool: 1
 - Zenoh: 1
 ---

content/learning-paths/iot/_index.md

@@ -12,11 +12,11 @@ subjects_filter:
 - CI-CD: 4
 - Containers and Virtualization: 2
 - Embedded Linux: 2
-- ML: 2
+- ML: 3
 - Performance and Architecture: 3
 operatingsystems_filter:
 - Baremetal: 4
-- Linux: 9
+- Linux: 10
 - macOS: 2
 - RTOS: 2
 - Windows: 2
@@ -28,13 +28,16 @@ tools_software_languages_filter:
 - Azure: 1
 - balenaCloud: 1
 - BalenaOS: 1
+- Bash: 1
 - C: 1
+- C++: 1
 - Docker: 2
 - FVP: 1
 - GitHub: 3
+- llama.cpp: 1
 - Matter: 1
 - MCP: 1
-- Python: 2
+- Python: 3
 - Raspberry Pi: 4
 - Remote.It: 1
 - ROS 2: 1

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

@@ -9,14 +9,14 @@ maintopic: true
 operatingsystems_filter:
 - Android: 2
 - ChromeOS: 2
-- Linux: 37
+- Linux: 38
 - macOS: 10
 - Windows: 46
 subjects_filter:
 - CI-CD: 6
 - Containers and Virtualization: 7
 - Migration to Arm: 30
-- ML: 4
+- ML: 5
 - Performance and Architecture: 28
 subtitle: Create and migrate apps for power efficient performance
 title: Laptops and Desktops
@@ -28,10 +28,10 @@ tools_software_languages_filter:
 - Arm Performance Libraries: 2
 - Arm64EC: 1
 - Assembly: 1
-- Bash: 2
+- Bash: 3
 - C: 10
 - C#: 6
-- C++: 11
+- C++: 12
 - CCA: 1
 - Clang: 13
 - CMake: 3
@@ -54,7 +54,7 @@ tools_software_languages_filter:
 - Kubernetes: 1
 - KVM: 1
 - Linux: 1
-- llama.cpp: 2
+- llama.cpp: 3
 - LLM: 1
 - LLVM: 2
 - llvm-mca: 1
@@ -67,7 +67,7 @@ tools_software_languages_filter:
 - OpenCV: 1
 - perf: 4
 - PowerShell: 1
-- Python: 8
+- Python: 9
 - QEMU: 1
 - Qt: 2
 - RDP: 1

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

@@ -9,14 +9,14 @@ key_ip:
 - Mali
 maintopic: true
 operatingsystems_filter:
-- Android: 32
-- Linux: 32
+- Android: 33
+- Linux: 33
 - macOS: 14
 - Windows: 14
 subjects_filter:
 - Gaming: 6
 - Graphics: 6
-- ML: 14
+- ML: 16
 - Performance and Architecture: 35
 subtitle: Optimize Android apps and build faster games using cutting-edge Arm tech
 title: Mobile, Graphics, and Gaming
@@ -31,10 +31,11 @@ tools_software_languages_filter:
 - Arm Mobile Studio: 1
 - Arm Performance Studio: 3
 - Assembly: 1
+- Bash: 1
 - Bazel: 1
-- C: 4
+- C: 5
 - C#: 3
-- C++: 13
+- C++: 14
 - CCA: 1
 - Clang: 12
 - CMake: 2
@@ -53,6 +54,7 @@ tools_software_languages_filter:
 - KleidiAI: 2
 - Kotlin: 8
 - LiteRT: 1
+- llama.cpp: 1
 - LLM: 1
 - LLVM: 1
 - llvm-mca: 1
@@ -61,7 +63,7 @@ tools_software_languages_filter:
 - NEON: 1
 - ONNX Runtime: 1
 - OpenGL ES: 1
-- Python: 5
+- Python: 7
 - PyTorch: 2
 - QEMU: 1
 - RenderDoc: 1

content/learning-paths/mobile-graphics-and-gaming/litert-sme/1-litert-kleidiai-sme2.md

@@ -20,7 +20,7 @@ The software stack for LiteRT is shown below.
 
 ## How KleidiAI works in LiteRT
 
-To understand how KleidiAI SME2 micro-kernels work in LiteRT, think about a LiteRT model with one fully connected operator using the FP32 data type. The following diagrams illustrate the execution workflow of XNNPACK’s implementation compared with the workflow when KleidiAI SME2 is enabled in XNNPACK.
+To understand how KleidiAI SME2 micro-kernels work in LiteRT, think about a LiteRT model with one fully connected operator using the FP32 data type. The following diagrams illustrate the execution workflow of XNNPACK's implementation compared with the workflow when KleidiAI SME2 is enabled in XNNPACK.
 
 ### LiteRT → XNNPACK workflow
 

content/learning-paths/mobile-graphics-and-gaming/litert-sme/2-build-tool.md

@@ -106,7 +106,7 @@ bazel-bin/litert/tools/benchmark_model
 
 ## Build the LiteRT benchmark tool without KleidiAI (baseline comparison)
 
-To compare the performance of the KleidiAI SME2 implementation against XNNPACK’s original implementation, build another version of the LiteRT benchmark tool without KleidiAI and SME2 enabled.
+To compare the performance of the KleidiAI SME2 implementation against XNNPACK's original implementation, build another version of the LiteRT benchmark tool without KleidiAI and SME2 enabled.
 
 Set the build options to disable SME2 and KleidiAI:
 
@@ -129,7 +129,7 @@ ${XNNPACK_OPTIONS} "${BENCHMARK_TOOL_PATH}" \
 --repo_env=HERMETIC_PYTHON_VERSION=3.12
 ```
 
-This build of the `benchmark_model` disables all SME2 micro-kernels and forces fallback to XNNPACK’s NEON or SVE2 kernels.
+This build of the `benchmark_model` disables all SME2 micro-kernels and forces fallback to XNNPACK's NEON or SVE2 kernels.
 
 You can then use Android Debug Bridge (ADB) to push the benchmark tool to your Android device:
 

content/learning-paths/mobile-graphics-and-gaming/litert-sme/3-build-model.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## KleidiAI SME2 support in LiteRT
 
-LiteRT uses XNNPACK as its default CPU backend. KleidiAI micro-kernels are integrated through XNNPACK in LiteRT. Only a subset of KleidiAI Scalable Matrix Extension (SME and SME2) micro-kernels has been integrated into XNNPACK. These micro-kernels support operators using the following data types and quantization configurations in the LiteRT model. Other operators use XNNPACK’s default implementation during inference.
+LiteRT uses XNNPACK as its default CPU backend. KleidiAI micro-kernels are integrated through XNNPACK in LiteRT. Only a subset of KleidiAI Scalable Matrix Extension (SME and SME2) micro-kernels has been integrated into XNNPACK. These micro-kernels support operators using the following data types and quantization configurations in the LiteRT model. Other operators use XNNPACK's default implementation during inference.
 
 ### Supported operator configurations
 
@@ -158,7 +158,7 @@ This quantization method quantizes both the activations and the weights to INT8.
 
 KleidiAI provides INT8 packing micro-kernels for both the activations and weights matrix, as well as INT8 matrix multiplication micro-kernels.
 
-## What you've accomplishee and what's next
+## What you've accomplished and what's next
 You have now created several LiteRT models with different quantization options, ready for benchmarking on your Arm-based Android device. You have:
 
 - Built a simple Keras model and converted it to LiteRT (`.tflite`) format