Merge pull request #2111 from madeline-underwood/naming_update

Changed TFLite -> LiteRT in everything but code and file names

Author: jasonrandrews

content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/2-overview.md

@@ -46,7 +46,7 @@ The low-light enhancement pipeline is adapted from the LiveHDR+ method originall
 
 ![example image alt-text#center](lle_pipeline.png "Low-Light Enhancement Pipeline Diagram")
 
-The Low-Resolution Coefficient Prediction Network (implemented with TFLite) performs computations such as:
+The Low-Resolution Coefficient Prediction Network (implemented with LiteRT) performs computations such as:
 - Strided convolutions.
 - Local feature extraction using convolutional layers.
 - Global feature extraction using convolutional and fully connected layers.

content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/3-build.md

@@ -74,9 +74,9 @@ Leave the container by pressing `Ctrl+D`.
 ## Notes on the cmake configuration options
 
 - `-DENABLE_SME2=$ENABLE_SME2` with `ENABLE_SME2=0`: SME2 (Scalable Matrix Extension 2) is disabled in this build (`0`).  
-- `-DARMNN_TFLITE_PARSER=0`: Configures the `ai-camera-pipelines` repository to use TFLite with XNNPack instead of ArmNN.
+- `-DARMNN_TFLITE_PARSER=0`: Configures the `ai-camera-pipelines` repository to use LiteRT with XNNPack instead of ArmNN.
 - `-DENABLE_KLEIDICV:BOOL=ON`: Enables KleidiCV for optimized image processing.
-- `-DXNNPACK_ENABLE_KLEIDIAI:BOOL=ON`:  Enables KleidiAI acceleration for TFLite workloads via XNNPack.
+- `-DXNNPACK_ENABLE_KLEIDIAI:BOOL=ON`:  Enables KleidiAI acceleration for LiteRT workloads via XNNPack.
 
 ## Install the pipelines
 

content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/5-performances.md

@@ -16,11 +16,11 @@ The application you built earlier includes a *benchmark mode* that runs the core
 These benchmarks demonstrate the performance improvements enabled by KleidiCV and KleidiAI:
 - KleidiCV enhances OpenCV performance with computation kernels optimized for Arm processors.
 
-- KleidiAI accelerates TFLite + XNNPack inference using AI-optimized micro-kernels tailored for Arm CPUs.
+- KleidiAI accelerates LiteRT + XNNPack inference using AI-optimized micro-kernels tailored for Arm CPUs.
 
 ## Performances with KleidiCV and KleidiAI
 
-By default, the OpenCV library is built with KleidiCV support, and TFLite+xnnpack is built with KleidiAI support. 
+By default, the OpenCV library is built with KleidiCV support, and LiteRT+xnnpack is built with KleidiAI support. 
 
 You can run the benchmarks using the applications you built earlier.