Merge pull request #2174 from pareenaverma/content_review

Tech review of ethosu corstone-320 fvp LP

Author: pareenaverma

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/2-env-setup.md

@@ -50,7 +50,7 @@ Run the commands below to set up the ExecuTorch internal dependencies:
 
 ```bash
 git submodule sync
-git submodule update --init
+git submodule update --init --recursive
 ./install_executorch.sh
 ```
 
@@ -70,7 +70,7 @@ pip list | grep executorch
 ```
 
 ```output
-executorch         0.6.0a0+3eea1f1
+executorch         0.8.0a0+92fb0cc
 ```
 
 ## Next Steps

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-env-setup-fvp.md

@@ -18,7 +18,7 @@ The Corstone reference system is provided free of charge, although you will have
 
 {{% notice macOS %}}
 
-Setting up FVPs on MacOS requires some extra steps, outlined in GitHub repo [VPs-on-Mac](https://github.com/Arm-Examples/FVPs-on-Mac/). macOS users must do this first, before setting up the Corestone-320 FVP.
+Setting up FVPs on MacOS requires some extra steps, outlined in GitHub repo [FVPs-on-Mac](https://github.com/Arm-Examples/FVPs-on-Mac/). macOS users must do this first, before setting up the Corstone-320 FVP.
 
 {{% /notice %}}
 
@@ -49,4 +49,4 @@ Test that the setup was successful by running the `run.sh` script for Ethos-U85,
 
 You will see a number of examples run on the FVP.
 
-This confirms the installation, so you can now proceed to the Learning Path [Build a Simple PyTorch Model](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/4-build-model/).
+This confirms the installation, so you can now proceed to the next section.

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/4-how-executorch-works.md

@@ -8,7 +8,8 @@ weight: 5 # 1 is first, 2 is second, etc.
 layout: "learningpathall"
 ---
 
-Source: [How ExecuTorch Works](https://docs.pytorch.org/executorch/stable/intro-how-it-works.html) (official PyTorch Documentation)
+To get a better understanding of [How ExecuTorch Works](https://docs.pytorch.org/executorch/stable/intro-how-it-works.html) refer to the official PyTorch Documentation. A summary is provided here for your reference:
+
 1. **Export the model:**
    * Generate a Graph
    * A graph is series of operators (ReLU, quantize, etc.) eligible for delegation to an accelerator

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/5-configure-fvp-gui.md

@@ -12,15 +12,9 @@ layout: "learningpathall"
 
 Note down your computer's IP address:
 ```bash
-ip addr show <YOUR_INTERFACE_NAME>
+ip addr show 
 ```
-To help you, here are some common WiFi interface names on Linux:
-|Interface Name|Meaning / Context|
-|--------------|-----------------|
-|wlan0|Legacy name (common on older systems)|
-|wlp2s0, wlp3s0|Predictable network naming scheme (modern systems)|
-|wlx<MAC>|Some systems name interfaces after MAC addresses|
-|wifi0, ath0|Very rare, specific to certain drivers (e.g., Atheros)|
+Note down the IP address of your active network interface (inet) which you will use later to pass as an argument to the FVP.
 
 {{% notice macOS %}}
 
@@ -34,7 +28,7 @@ ipconfig getifaddr en0 # Returns your Mac's WiFi IP address
 
 ## Enable the FVP's GUI
 
-Edit the following parameters in [run_fvp.sh](https://github.com/pytorch/executorch/blob/d5fe5faadb8a46375d925b18827493cd65ec84ce/backends/arm/scripts/run_fvp.sh#L97-L102), to enable the Mobilenet V2 output on the FVP's GUI:
+Edit the following parameters in your locally checked out [executorch/backends/arm/scripts/run_fvp.sh](https://github.com/pytorch/executorch/blob/d5fe5faadb8a46375d925b18827493cd65ec84ce/backends/arm/scripts/run_fvp.sh#L97-L102) file, to enable the Mobilenet V2 output on the FVP's GUI:
 
 ```bash
 -C mps4_board.subsystem.ethosu.num_macs=${num_macs} \

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/6-evaluate-output.md

@@ -161,3 +161,5 @@ I [executorch:arm_perf_monitor.cpp:184] ethosu_pmu_cntr4 : 130
 |ethosu_pmu_cntr2|External DRAM read beats(ETHOSU_PMU_EXT_RD_DATA_BEAT_RECEIVED)|Number of data beats read from off-chip memory (e.g., DRAM). Often 0 if Sram_Only is used.|If non-zero, may indicate cache misses or large model size.|
 |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.|
+
+In this learning path you have successfully learnt how to deploy a MobileNet V2 Model using ExecuTorch on Arm's Corstone-320 FVP.

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

@@ -7,11 +7,11 @@ cascade:
 
 minutes_to_complete: 120
 
-who_is_this_for: This is an introductory topic for developers and data scientists new to Tiny Machine Learning (TinyML), who want to visualize ExecuTorch performance on a virtual device.
+who_is_this_for: This is an introductory topic for developers and data scientists new to Tiny Machine Learning (TinyML), who want to understand and visualize ExecuTorch performance on a virtual device.
 
 learning_objectives:
     - Identify suitable Arm-based devices for TinyML applications.
-    - Optionally, install Fixed Virtual Platforms (FVPs) on MacOS.
+    - Install Fixed Virtual Platforms (FVPs).
     - Deploy a TinyML ExecuTorch model to a Corstone-320 FVP.
     - Observe model execution on the FVP's graphical user interface (GUI).