MLECO-1913: Documentation update: helper scripts and AD use case model update

Change-Id: I610b720146b520fe8633d25255b97df647b99ef5
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>

1 files changed, 130 insertions, 62 deletions

diff --git a/docs/quick_start.md b/docs/quick_start.md
index f3565e8..abf8f50 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -1,8 +1,9 @@
 # Quick start example ML application
 
-This is a quick start guide that will show you how to run the keyword spotting example application. The aim of this guide
-is to illustrate the flow of running an application on the evaluation kit rather than showing the keyword spotting
-functionality or performance. All use cases in the evaluation kit follow the steps.
+This is a quick start guide that will show you how to run the keyword spotting example application.
+The aim of this quick start guide is to enable you to run an application quickly on the Fixed Virtual Platform.
+The assumption we are making is that your Arm® Ethos™-U55 NPU is configured to use 128 Multiply-Accumulate units,
+is using a shared SRAM with the Arm® Cortex®-M55.  
 
 1. Verify you have installed [the required prerequisites](sections/building.md#Build-prerequisites).
 
@@ -19,75 +20,142 @@ functionality or performance. All use cases in the evaluation kit follow the ste
     git submodule update --init
     ```
 
-4. Next, you would need to get a neural network model. For the purpose of this quick start guide, we'll use the
-    `ds_cnn_clustered_int8` keyword spotting model from the [Arm public model zoo](https://github.com/ARM-software/ML-zoo)
-    and the principle remains the same for all of the other use cases. Download the `ds_cnn_large_int8.tflite` model
-    file with the curl command below:
-
-    ```commandline
-    curl -L https://github.com/ARM-software/ML-zoo/blob/master/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite?raw=true --output ds_cnn_clustered_int8.tflite
-    ```
-
-5. [Vela](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela) is an open-source python tool converting
+4. Next, you can use the `build_default` python script to get the default neural network models, compile them with
+    Vela and build the project.
+    [Vela](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela) is an open-source python tool converting
     TensorFlow Lite for Microcontrollers neural network model into an optimized model that can run on an embedded system
     containing an Ethos-U55 NPU. It is worth noting that in order to take full advantage of the capabilities of the NPU, the
     neural network operators should be [supported by Vela](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/HEAD/SUPPORTED_OPS.md).
-    In this step, you will compile the model with Vela.
-
-    For this step, you need to ensure you have [correctly installed the Vela package](https://pypi.org/project/ethos-u-vela/):
-
-    ```commandline
-    python3 -m venv env
-    source ./env/bin/activate
-    pip install --upgrade pip
-    pip install --upgrade setuptools
-    pip install ethos-u-vela
-    ```
-
-    In the command below, we specify that we are using the Arm® Ethos™-U55 NPU with a 128 Multiply-Accumulate units
-    (MAC units) configured for a High End Embedded use case. The [building section](sections/building.md#Optimize-custom-model-with-Vela-compiler)
-    has more detailed explanation about Vela usage.
-
-    ```commandline
-    vela ds_cnn_clustered_int8.tflite \
-        --accelerator-config=ethos-u55-128 \
-        --block-config-limit=0 \
-        --config scripts/vela/vela.ini \
-        --memory-mode Shared_Sram \
-        --system-config Ethos_U55_High_End_Embedded
-    ```
-
-    An optimized model file for Ethos-U55 is generated in a folder named `output`.
-
-6. Create a `build` folder in the root level of the evaluation kit.
-
-    ```commandline
-    mkdir build && cd build
-    ```
-
-7. Build the makefiles with `CMake` as shown in the command below. The [build process section](sections/building.md#Build-process)
-    gives an in-depth explanation about the meaning of every parameter. For the time being, note that we point the Vela
-    optimized model from stage 5 in the `-Dkws_MODEL_TFLITE_PATH` parameter.
-
-    ```commandline
-    cmake \
-        -DUSE_CASE_BUILD=kws \
-        -Dkws_MODEL_TFLITE_PATH=output/ds_cnn_clustered_int8_vela.tflite \
-        ..
-    ```
-
-8. Compile the project with a `make`. Details about this stage can be found in the [building part of the documentation](sections/building.md#Building-the-configured-project).
 
     ```commandline
-    make -j4
+    python3 build_default.py
     ```
 
-9. Launch the project as explained [here](sections/deployment.md#Deployment). In this quick-start guide, we'll use the Fixed
-    Virtual Platform. Point the generated `bin/ethos-u-kws.axf` file in stage 8 to the FVP that you have downloaded when
+5. Launch the project as explained [here](sections/deployment.md#Deployment). For the purpose of this quick start guide,
+    we'll use the keyword spotting application and the Fixed Virtual Platform.
+    Point the generated `bin/ethos-u-kws.axf` file in stage 4 to the FVP that you have downloaded when
     installing the prerequisites.
 
     ```commandline
     <path_to_FVP>/FVP_Corstone_SSE-300_Ethos-U55 -a ./bin/ethos-u-kws.axf
     ```
 
-10. A telnet window is launched through which you can interact with the application and obtain performance figures.
+6. A telnet window is launched through which you can interact with the application and obtain performance figures.
+
+> **Note:**: Execution of the build_default.py script is equivalent to running the following commands:
+
+```commandline
+mkdir resources_downloaded && cd resources_downloaded
+python3 -m venv env
+env/bin/python3 -m pip install --upgrade pip
+env/bin/python3 -m pip install --upgrade setuptools
+env/bin/python3 -m pip install ethos-u-vela==2.1.1
+cd ..
+
+curl -L https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite \
+    --output resources_downloaded/ad/ad_medium_int8.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input/0.npy \
+    --output ./resources_downloaded/ad/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity/0.npy \
+    --output ./resources_downloaded/ad/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite \
+    --output ./resources_downloaded/asr/wav2letter_int8.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy \
+    --output ./resources_downloaded/asr/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8/0.npy \
+    --output ./resources_downloaded/asr/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/mobilenet_v2_1.0_224_quantized_1_default_1.tflite \
+    --output ./resources_downloaded/img_class/mobilenet_v2_1.0_224_quantized_1_default_1.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_input/input/0.npy \
+    --output ./resources_downloaded/img_class/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_output/output/0.npy \
+    --output ./resources_downloaded/img_class/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \
+    --output ./resources_downloaded/kws/ds_cnn_clustered_int8.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy \
+    --output ./resources_downloaded/kws/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \
+    --output ./resources_downloaded/kws/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite \
+    --output ./resources_downloaded/kws_asr/wav2letter_int8.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy \
+    --output ./resources_downloaded/kws_asr/asr/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy
+    --output ./resources_downloaded/kws_asr/asr/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8/0.npy \
+    --output ./resources_downloaded/kws_asr/asr/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite \
+    --output ./resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy \
+    --output ./resources_downloaded/kws_asr/kws/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy \
+    --output ./resources_downloaded/kws_asr/kws/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/dnn_s_quantized.tflite \
+    --output ./resources_downloaded/inference_runner/dnn_s_quantized.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws/ds_cnn_clustered_int8.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/kws
+mv resources_downloaded/kws/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws/ds_cnn_clustered_int8_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/wav2letter_int8.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/kws_asr
+mv resources_downloaded/kws_asr/wav2letter_int8_vela.tflite resources_downloaded/kws_asr/wav2letter_int8_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/kws_asr/ds_cnn_clustered_int8.tflite -\
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/kws_asr
+mv resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela.tflite resources_downloaded/kws_asr/ds_cnn_clustered_int8_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/inference_runner/dnn_s_quantized.tflite -\
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/inference_runner
+mv resources_downloaded/inference_runner/dnn_s_quantized_vela.tflite resources_downloaded/inference_runner/dnn_s_quantized_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/img_class/mobilenet_v2_1.0_224_quantized_1_default_1.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/img_class
+mv resources_downloaded/img_class/mobilenet_v2_1.0_224_quantized_1_default_1_vela.tflite resources_downloaded/img_class/mobilenet_v2_1.0_224_quantized_1_default_1_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/asr/wav2letter_int8.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/asr
+mv resources_downloaded/asr/wav2letter_int8_vela.tflite resources_downloaded/asr/wav2letter_int8_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/ad/ad_medium_int8.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --block-config-limit=0 --config scripts/vela/default_vela.ini \
+    --memory-mode=Shared_Sram \
+    --system-config=Ethos_U55_High_End_Embedded \
+    --output-dir=resources_downloaded/ad
+mv resources_downloaded/ad/ad_medium_int8_vela.tflite resources_downloaded/ad/ad_medium_int8_vela_H128.tflite
+
+mkdir cmake-build-mps3-sse-300-gnu-release and cd cmake-build-mps3-sse-300-gnu-release
+
+cmake .. \
+    -DTARGET_PLATFORM=mps3 \
+    -DTARGET_SUBSYSTEM=sse-300 \
+    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-gcc.cmake
+```
+
+> **Note:** If you want to make changes to the application (for example modifying the number of MAC units of the Ethos-U or running a custom neural network),
+> you should follow the approach defined in [documentation.md](../docs/documentation.md) instead of using the `build_default` python script.