Final

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/6-run-model.md

@@ -7,12 +7,10 @@ weight: 6 # 1 is first, 2 is second, etc.
 # Do not modify these elements
 layout: "learningpathall"
 ---
-## Deploy a TinyML Model
+## Deploy Mobilenet V2 with ExecuTorch
 
 With your environment and FVP now set up, you're ready to deploy and run a real TinyML model using ExecuTorch.
 
-## Deploy Mobilenet V2 with ExecuTorch
-
 This example deploys the [MobileNet V2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/) computer vision model. The model is a convolutional neural network (CNN) that extracts visual features from an image. It is used for image classification and object detection.
 
 The Python code for the MobileNet V2 model is in your local `executorch` repo: [executorch/examples/models/mobilenet_v2/model.py](https://github.com/pytorch/executorch/blob/main/examples/models/mobilenet_v2/model.py). You can deploy it using [run.sh](https://github.com/pytorch/executorch/blob/main/examples/arm/run.sh), just like you did in the previous step, with some extra parameters:

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/8-evaluate-output.md

@@ -8,7 +8,9 @@ weight: 8 # 1 is first, 2 is second, etc.
 layout: "learningpathall"
 ---
 
-Now that you've successfully run the MobileNet V2 model on the Corstone-320 FVP, this section shows how to read and interpret performance data output by ExecuTorch.
+## Interpreting the results
+
+Now that you've successfully deployed and executed the MobileNet V2 model on the Corstone-320 FVP, this section walks you through how to interpret the resulting performance data. This includes inference time, operator delegation, and hardware-level metrics from the Ethos-U NPU.
 
 ## Observe Ahead-of-Time Compilation
 - The following output from [run.sh](https://github.com/pytorch/executorch/blob/main/examples/arm/run.sh) confirms that Ahead-of-Time (AOT) compilation was successful.
@@ -17,7 +19,7 @@ Now that you've successfully run the MobileNet V2 model on the Corstone-320 FVP,
 
 {{% notice Note %}}
 
-In the below sample outputs, the `executorch` directory path is indicated as `/path/to/executorch`. Your actual path will depend on where you cloned your local copy of the [executorch repo](https://github.com/pytorch/executorch/tree/main).
+In the examples below, `/path/to/executorch` represents the directory where you cloned your local copy of the [ExecuTorch repo](https://github.com/pytorch/executorch/tree/main). Replace it with your actual path when running commands or reviewing output.
 
 {{% /notice %}}
 
@@ -164,6 +166,6 @@ I [executorch:arm_perf_monitor.cpp:184] ethosu_pmu_cntr4 : 130
 |ethosu_pmu_cntr3|External DRAM write beats(ETHOSU_PMU_EXT_WR_DATA_BEAT_WRITTEN)|Number of write data beats to external memory.|Helps detect offloading or insufficient SRAM.|
 |ethosu_pmu_cntr4|Idle cycles(ETHOSU_PMU_NPU_IDLE)|Number of cycles where the NPU had no work scheduled (i.e., idle).|High idle count = possible pipeline stalls or bad scheduling.|
 
-## Summary
+## Review
 
 In this Learning Path, you have learned how to deploy a MobileNet V2 model using ExecuTorch on Arm's Corstone-320 FVP. You're now ready to apply what you've learned to other models and configurations using ExecuTorch.

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_index.md

@@ -38,6 +38,7 @@ tools_software_languages:
     - ExecuTorch
     - Arm Compute Library
     - GCC
+    - Docker
 
 further_reading:
     - resource: