MLECO-2354 MLECO-2355 MLECO-2356: Moving noise reduction to public repository

* Use RNNoise model from PMZ
* Add Noise reduction use-case

Signed-off-by: Richard burton <richard.burton@arm.com>
Change-Id: Ia8cc7ef102e22a5ff8bfbd3833594a4905a66057

4 files changed, 588 insertions, 6 deletions

diff --git a/docs/documentation.md b/docs/documentation.md
index a186fbb..0642075 100644
--- a/docs/documentation.md
+++ b/docs/documentation.md
@@ -206,11 +206,12 @@ What these folders contain:
 
 The models used in the use-cases implemented in this project can be downloaded from: [Arm ML-Zoo](https://github.com/ARM-software/ML-zoo).
 
-- [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8).
-- [DS-CNN](https://github.com/ARM-software/ML-zoo/tree/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b//models/keyword_spotting/ds_cnn_large/tflite_clustered_int8).
-- [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8).
-- [Anomaly Detection](https://github.com/ARM-software/ML-zoo/tree/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8).
-- [Visual Wake Word](https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite).
+- [Mobilenet V2](https://github.com/ARM-software/ML-zoo/tree/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8)
+- [DS-CNN](https://github.com/ARM-software/ML-zoo/tree/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b//models/keyword_spotting/ds_cnn_large/tflite_clustered_int8)
+- [Wav2Letter](https://github.com/ARM-software/ML-zoo/tree/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8)
+- [MicroNet for Anomaly Detection](https://github.com/ARM-software/ML-zoo/tree/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8)
+- [MicroNet for Visual Wake Word](https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite)
+- [RNNoise](https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite)
 
 When using *Ethos-U* NPU backend, Vela compiler optimizes the the NN model. However, if not and it is supported by
 TensorFlow Lite Micro, then it falls back on the CPU and execute.
diff --git a/docs/quick_start.md b/docs/quick_start.md
index 3488447..7613912 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -102,6 +102,26 @@ curl -L https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea
     --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/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite \
+    --output ./resources_downloaded/noise_reduction/rnnoise_INT8.tflite
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/main_input_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ifm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/vad_gru_prev_state_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ifm1.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/noise_gru_prev_state_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ifm2.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/denoise_gru_prev_state_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ifm3.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ofm0.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_1_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ofm1.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_2_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ofm2.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_3_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ofm3.npy
+curl -L https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_4_int8/0.npy \
+    --output ./resources_downloaded/noise_reduction/ofm4.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
 
@@ -217,6 +237,38 @@ mv resources_downloaded/ad/ad_medium_int8_vela.tflite resources_downloaded/ad/ad
     --output-dir=resources_downloaded/ad
 mv resources_downloaded/ad/ad_medium_int8_vela.tflite resources_downloaded/ad/ad_medium_int8_vela_Y256.tflite
 
+. resources_downloaded/env/bin/activate && vela resources_downloaded/vww/vww4_128_128_INT8.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --optimise Performance --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/vww/vww4_128_128_INT8_vela.tflite resources_downloaded/vww/vww4_128_128_INT8_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/vww/vww4_128_128_INT8.tflite \
+    --accelerator-config=ethos-u65-256 \
+    --optimise Performance --config scripts/vela/default_vela.ini \
+    --memory-mode=Dedicated_Sram \
+    --system-config=Ethos_U65_High_End \
+    --output-dir=resources_downloaded/ad
+mv resources_downloaded/vww/vww4_128_128_INT8_vela.tflite resources_downloaded/vww/vww4_128_128_INT8_vela_Y256.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/noise_reduction/rnnoise_INT8.tflite \
+    --accelerator-config=ethos-u55-128 \
+    --optimise Performance --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/noise_reduction/rnnoise_INT8_vela.tflite resources_downloaded/noise_reduction/rnnoise_INT8_vela_H128.tflite
+
+. resources_downloaded/env/bin/activate && vela resources_downloaded/noise_reduction/rnnoise_INT8.tflite \
+    --accelerator-config=ethos-u65-256 \
+    --optimise Performance --config scripts/vela/default_vela.ini \
+    --memory-mode=Dedicated_Sram \
+    --system-config=Ethos_U65_High_End \
+    --output-dir=resources_downloaded/ad
+mv resources_downloaded/noise_reduction/rnnoise_INT8_vela.tflite resources_downloaded/noise_reduction/rnnoise_INT8_vela_Y256.tflite
+
 mkdir cmake-build-mps3-sse-300-gnu-release and cd cmake-build-mps3-sse-300-gnu-release
 
 cmake .. \
diff --git a/docs/sections/arm_virtual_hardware.md b/docs/sections/arm_virtual_hardware.md
index 2f05525..ca60a28 100644
--- a/docs/sections/arm_virtual_hardware.md
+++ b/docs/sections/arm_virtual_hardware.md
@@ -23,5 +23,5 @@ Note that you can register to receive free AWS credits to use Arm Virtual Hardwa
 
 You can find more information about Arm Virtual Hardware [here](https://arm-software.github.io/VHT/main/overview/html/index.html).
 
-Once you have access to the AWS instance, we recommend starting from the [quick start guide](../quick_start.md) in order to get familiar
+Once you have access to the AWS instance, we recommend starting from the [quick start guide](../quick_start.md#Quick-start-example-ML-application) in order to get familiar
 with the ml-embedded-evaluation-kit. Note that on the AWS instance, the FVP is available under `/opt/FVP_Corstone_SSE-300`.
diff --git a/docs/use_cases/noise_reduction.md b/docs/use_cases/noise_reduction.md
new file mode 100644
index 0000000..e6df89c
--- /dev/null
+++ b/docs/use_cases/noise_reduction.md
@@ -0,0 +1,529 @@
+# Noise Reduction Code Sample
+
+- [Noise Reduction Code Sample](#noise-reduction-code-sample)
+  - [Introduction](#introduction)
+  - [How the default neural network model works](#how-the-default-neural-network-model-works)
+  - [Post-processing](#post_processing)
+    - [Dumping of memory contents from the Fixed Virtual Platform](#dumping-of-memory-contents-from-the-fixed-virtual-platform)
+    - [Dumping post processed results for all inferences](#dumping-post_processed-results-for-all-inferences)
+  - [Prerequisites](#prerequisites)
+  - [Building the code sample application from sources](#building-the-code-sample-application-from-sources)
+    - [Build options](#build-options)
+    - [Build process](#build-process)
+    - [Add custom input](#add-custom-input)
+    - [Add custom model](#add-custom-model)
+  - [Setting up and running Ethos-U NPU code sample](#setting-up-and-running-ethos_u-npu-code-sample)
+    - [Setting up the Ethos-U NPU Fast Model](#setting-up-the-ethos_u-npu-fast-model)
+    - [Starting Fast Model simulation](#starting-fast-model-simulation)
+    - [Running Noise Reduction](#running-noise-reduction)
+
+## Introduction
+
+This document describes the process of setting up and running the Arm® Ethos™-U NPU Noise Reduction
+example.
+
+Use case code is stored in the following directory: [source/use_case/noise_reduction](../../source/use_case/noise_reduction).
+
+## How the default neural network model works
+
+Instead of replicating a "noisy audio in" and "clean audio out" problem, a simpler version is
+defined. We use different frequency bands for the audio (22 in the original paper
+[RNNoise: Learning Noise Suppression](https://jmvalin.ca/demo/rnnoise/)). It is based on a scale like the "Mel scale"
+or "Bark scale" and calculates the energies for each band. Using this type of scale, the bands get
+divided up and the result is based on what is important to the human ear.
+
+When we have a noisy audio clip, the model takes the energy levels of these different bands as
+input. The model then tries to predict a value (called a gain), to apply to each frequency band. It
+is expected that applying this gain to each band brings the audio back to what a "clean" audio
+sample would have been like. It is like a 22-band equalizer, where we quickly adjust the level of
+each band so that the noise is removed. However, the signal, or speech, still passes through.
+
+In addition to the 22 band values calculated, the input features also include:
+
+- First and second derivatives of the first 6 coefficients,
+- The pitch period (1/frequency),
+- The pitch gain for six bands,
+- A value used to detect if speech is occurring.
+
+This provides 42 feature inputs, `22 + 6 + 6 + 1 + 6 + 1 = 42`, and the model produces `22` (gain
+values) outputs.
+
+> **Note:** The model also has a second output that predicts if speech is occurring in the given
+> sample.
+
+The pre-processing works in a windowed fashion, on 20ms of the audio clip at a time, and the stride
+is 10ms. So, for example, if we provide one second of audio this gives us `1000ms/10ms = 100` windows of
+features and, therefore, an input shape of `100x42` to the model. The output shape of the model is
+then `100x22`, representing the gain values to apply to each of the 100 windows.
+
+These output gain values can then be applied to each corresponding window of the noisy audio clip,
+producing a cleaner output.
+
+For more information please refer to the original paper: 
+[A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://arxiv.org/pdf/1709.08243.pdf)
+
+## Post-processing
+
+After each inference the output of the model is passed to post-processing code which uses the gain values the model
+produced to generate audio with the noise removed from it.
+
+For you to verify the outputs of the model after post-processing, you will have manually use an [offline script](../../scripts/py/rnnoise_dump_extractor.py)
+to convert the post-processed outputs into a wav file.
+This offline script takes a dump file as the input and saves the denoised WAV file to disk. The following is an example
+of how to call the script from the command line after running the use-case and
+[selecting to dump memory contents](#dumping-post_processed-results-for-all-inferences).
+
+```commandline
+python scripts/py/rnnoise_dump_extractor.py --dump_file <path_to_dump_file.bin> --output_dir <path_to_output_folder>
+```
+
+The application for this use case has been written to dump the post-processed output to the address pointed to by
+the CMake parameter `noise_reduction_MEM_DUMP_BASE_ADDR`. The default value is set to `0x80000000`.
+
+### Dumping of memory contents from the Fixed Virtual Platform
+
+The fixed virtual platform supports dumping of memory contents to a file. This can be done by
+specifying command-line arguments when starting the FVP executable. For example, the argument:
+
+```commandline
+$ FVP_Corstone_SSE-300_Ethos-U55 -a ./bin/ethos-u-noise_reduction.axf \
+    --dump cpu0=output.bin@Memory:0x80000000,0x100000
+```
+
+Dumps 1 MiB worth of data from address `0x80000000` to the file `output.bin`.
+
+### Dumping post-processed results for all inferences
+
+The Noise Reduction application uses the memory address specified by
+`noise_reduction_MEM_DUMP_BASE_ADDR` as a buffer to store post-processed results from all inferences. 
+The maximum size of this buffer is set by the parameter
+`noise_reduction_MEM_DUMP_LEN` which defaults to 1 MiB.
+
+Logging information is generated for every inference run performed. Each line corresponds to the post-processed
+result of that inference being written to a certain location in memory.
+
+For example:
+
+```log
+INFO - Audio Clip dump header info (20 bytes) written to 0x80000000
+INFO - Inference 1/136
+INFO - Copied 960 bytes to 0x80000014
+...
+INFO - Inference 136/136
+INFO - Copied 960 bytes to 0x8001fa54
+```
+
+In the preceding output we can see that it starts at the default address of
+`0x80000000` where some header information is dumped. Then, after the first inference 960 bytes 
+(480 INT16 values) are written to the first address after the dumped header `0x80000014`.
+Each inference afterward will then write another 960 bytes to the next address and so on until all inferences
+are complete.
+
+When consolidating all inference outputs for an entire audio clip, the application output should report:
+
+```log
+INFO - Output memory dump of 130580 bytes written at address 0x80000000
+```
+
+The application output log states that there are 130580 bytes worth of valid data ready to be read
+from `0x80000000`. If the FVP was started with the `--dump` option, then the output file is created
+when the FVP instance exits.
+
+## Prerequisites
+
+See [Prerequisites](../documentation.md#prerequisites)
+
+## Building the code sample application from sources
+
+### Build options
+
+In addition to the already specified build option in the main documentation, keyword spotting use
+case adds:
+
+- `noise_reduction_MODEL_TFLITE_PATH` - The path to the NN model file in *TFLite* format. The model
+  is processed and is included in the application axf file. The default value points to one of the
+  delivered set of models. Note that the parameter
+  `ETHOS_U_NPU_ENABLED` must be aligned with the chosen model. Therefore:
+  - if `ETHOS_U_NPU_ENABLED` is set to `On` or `1`, we assume that the NN model is optimized. The
+    model naturally falls back to the Arm® Cortex®-M CPU if an unoptimized model is supplied.
+  - if `ETHOS_U_NPU_ENABLED` is set to `Off` or `0`, then we assume that the NN model is unoptimized.
+    In this case, supplying an optimized model results in a runtime error.
+
+- `noise_reduction_FILE_PATH`: The path to the directory containing WAV files, or a path to single
+  WAV file, to be used in the application. The default value points to the
+  `resources/noise_reduction/samples` folder containing the delivered set of audio clips.
+
+- `noise_reduction_AUDIO_RATE`: The input data sampling rate. Each audio file from `noise_reduction_FILE_PATH` is 
+  preprocessed during the build to match the NN model input requirements. The default value is `48000`.
+
+- `noise_reduction_AUDIO_MONO`: If set to `ON`, then the audio data is converted to mono. The default value is `ON`.
+
+- `noise_reduction_AUDIO_OFFSET`: Begins loading audio data and starts from this specified offset, defined in seconds. 
+  The default value is set to `0`.
+
+- `noise_reduction_AUDIO_DURATION`: The length of the audio data to be used in the application in seconds. 
+  The default is `0`, meaning that the whole audio file is used.
+
+- `noise_reduction_AUDIO_MIN_SAMPLES`: Minimum number of samples required by the network model. If the audio clip is shorter than
+  this number, then it is padded with zeros. The default value is `480`.
+
+- `noise_reduction_ACTIVATION_BUF_SZ`: The intermediate, or activation, buffer size reserved for the
+  neural network model. By default, it is set to 2MiB.
+
+To **ONLY** build a `noise_reduction` example application, add `-DUSE_CASE_BUILD=noise_reduction`
+  (as specified in [Building](../documentation.md#Building) to the `cmake` command line).
+
+### Build process
+
+> **Note:** This section describes the process for configuring the build for `MPS3: SSE-300`. To
+> configure a different target platform, please see the [Building](../documentation.md#Building)
+> section.
+
+To **only** build the `noise_reduction` example, create a build directory, and then navigate inside.
+For example:
+
+```commandline
+mkdir build_noise_reduction && cd build_noise_reduction
+```
+
+On Linux, when providing only the mandatory arguments for CMake configuration, use the following
+command to build the Noise Reduction application to run on the *Ethos-U55* Fast Model:
+
+```commandline
+cmake ../ -DUSE_CASE_BUILD=noise_reduction
+```
+
+To configure a build that can be debugged using Arm DS, we specify the build type as `Debug` and use
+the `Arm Compiler` toolchain file:
+
+```commandline
+cmake .. \
+    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \
+    -DCMAKE_BUILD_TYPE=Debug \
+    -DUSE_CASE_BUILD=noise_reduction
+```
+
+For more notes, please refer to:
+
+- [Configuring with custom TPIP dependencies](../sections/building.md#configuring-with-custom-tpip-dependencies)
+- [Using Arm Compiler](../sections/building.md#using-arm-compiler)
+- [Configuring the build for simple-platform](../sections/building.md#configuring-the-build-for-simple_platform)
+- [Working with model debugger from Arm Fast Model Tools](../sections/building.md#working-with-model-debugger-from-arm-fast-model-tools)
+- [Building for different Ethos-U variants](../sections/building.md#building-for-different-ethos_u-npu-variants)
+
+> **Note:** If you are rebuilding with changed parameters values, it is highly advised that you
+> clean the build directory and rerun the CMake command.
+
+If the CMake command is successful, then build the application as follows:
+
+```commandline
+make -j4
+```
+
+> **Note:** To see compilation and link details, add `VERBOSE=1`.
+
+The build results are placed under the `build/bin` folder. For example:
+
+```tree
+bin
+ ├── ethos-u-noise_reduction.axf
+ ├── ethos-u-noise_reduction.htm
+ ├── ethos-u-noise_reduction.map
+ ├── images-noise_reduction.txt
+ └── sectors
+      └── noise_reduction
+           ├── dram.bin
+           └── itcm.bin
+```
+
+Based on the preceding output, the files contain the following information:
+
+- `ethos-u-noise_reduction.axf`: The built application binary for the noise reduction use case.
+
+- `ethos-u-noise_reduction.map`: Information from building the application (for example. The
+  libraries used, what was optimized, and location of objects).
+
+- `ethos-u-noise_reduction.htm`: A human readable file containing the call graph of application
+  functions.
+
+- `sectors/`: This folder contains the built application, which is split into files for loading into
+  different FPGA memory regions.
+
+- `Images-noise_reduction.txt`: Tells the FPGA which memory regions to use for loading the binaries
+  in the `sectors/...` folder.
+
+### Add custom input
+
+To run with inputs different to the ones supplied, the parameter `noise_reduction_FILE_PATH` can be
+pointed to a WAV file, or a directory containing WAV files. Once you have a directory with WAV files, 
+run the following command:
+
+```commandline
+cmake .. \
+    -DUSE_CASE_BUILD=noise_reduction \
+    -Dnoise_reduction_FILE_PATH=/path/to/custom/wav_files
+```
+
+### Add custom model
+
+The application performs inference using the model pointed to by the CMake parameter
+`noise_reduction_MODEL_TFLITE_PATH`.
+
+> **Note:** If you want to run the model using *Ethos-U* ensure that your custom model has been
+> run through the Vela compiler successfully before continuing.
+
+For further information: [Optimize model with Vela compiler](../sections/building.md#optimize-custom-model-with-vela-compiler).
+
+An example:
+
+```commandline
+cmake .. \
+    -Dnoise_reduction_MODEL_TFLITE_PATH=<path/to/custom_model_after_vela.tflite> \
+    -DUSE_CASE_BUILD=noise_reduction
+```
+
+> **Note** Changing the neural network model often also requires the pre-processing implementation
+> to be changed. Please refer to:
+> [How the default neural network model works](#how-the-default-neural-network-model-works).
+
+> **Note:** Before re-running the CMake command, clean the build directory.
+
+The `.tflite` model file, which is pointed to by `noise_reduction_MODEL_TFLITE_PATH`, is converted
+to C++ files during the CMake configuration stage. It is then compiled into the application for
+performing inference with.
+
+To see which model path was used, inspect the configuration stage log:
+
+```log
+-- User option noise_reduction_MODEL_TFLITE_PATH is set to <path/to/custom_model_after_vela.tflite>
+...
+-- Using <path/to/custom_model_after_vela.tflite>
+++ Converting custom_model_after_vela.tflite to custom_model_after_vela.tflite.cc
+-- Generating labels file from <path/to/labels_custom_model.txt>
+-- writing to <path/to/build/generated/src/Labels.cc>
+...
+```
+
+After compiling, your custom model replaces the default one in the application.
+
+## Setting up and running Ethos-U NPU code sample
+
+### Setting up the Ethos-U NPU Fast Model
+
+The FVP is available publicly from [Arm Ecosystem FVP downloads](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps).
+
+For the *Ethos-U* evaluation, please download the MPS3 based version of the Arm® *Corstone™-300* model that contains *Cortex-M55*
+and offers a choice of the *Ethos-U55* and *Ethos-U65* processors.
+
+To install the FVP:
+
+- Unpack the archive,
+
+- Run the install script in the extracted package:
+
+```commandline
+$./FVP_Corstone_SSE-300.sh
+```
+
+- Follow the instructions to install the FVP to your required location.
+
+### Starting Fast Model simulation
+
+Once the building step has completed, the application binary `ethos-u-noise_reduction.axf` can be
+found in the `build/bin` folder. Assuming the install location of the FVP was set to
+`~/FVP_install_location`, start the simulation with the following command:
+
+```commandline
+~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/mps3-sse-300/ethos-u-noise_reduction.axf
+```
+
+A log output then appears on the terminal:
+
+```log
+telnetterminal0: Listening for serial connection on port 5000
+telnetterminal1: Listening for serial connection on port 5001
+telnetterminal2: Listening for serial connection on port 5002
+telnetterminal5: Listening for serial connection on port 5003
+```
+
+This also launches a telnet window with the standard output of the sample application. It also
+includes error log entries containing information about the pre-built application version,
+TensorFlow Lite Micro library version used, and the data type. As well as the input and output
+tensor sizes of the model that was compiled into the executable binary.
+
+After the application has started, if `noise_reduction_FILE_PATH` pointed to a single file (or a
+folder containing a single input file), then the inference starts immediately. If multiple inputs
+are chosen, then a menu is output and waits for the user input from telnet terminal.
+
+For example:
+
+```log
+User input required
+Enter option number from:
+
+  1. Run noise reduction on the next WAV
+  2. Run noise reduction on a WAV at chosen index
+  3. Run noise reduction on all WAVs
+  4. Show NN model info
+  5. List audio clips
+
+Choice:
+```
+
+1. “Run noise reduction on the next WAV”: Runs processing and inference on the next in line WAV file.
+
+    > **Note:** Depending on the size of the input WAV file, multiple inferences can be invoked.
+
+2. “Run noise reduction on a WAV at chosen index”: Runs processing and inference on the WAV file
+   corresponding to the chosen index.
+
+    > **Note:** Select the index in the range of supplied WAVs during application build. By default,
+    the pre-built application has three files and indexes from 0-2.
+
+3. “Run noise reduction on all WAVs”: Triggers sequential processing and inference executions on 
+   all baked-in WAV files.
+
+4. “Show NN model info”: Prints information about the model data type, including the input and
+   output tensor sizes. For example:
+
+    ```log
+    INFO - Model info:
+    INFO - Model INPUT tensors:
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 42 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:   1
+    INFO -          2:  42
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.221501
+    INFO - ZeroPoint[0] = 14
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 24 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:  24
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.007843
+    INFO - ZeroPoint[0] = -1
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 48 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:  48
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.047942
+    INFO - ZeroPoint[0] = -128
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 96 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:  96
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.007843
+    INFO - ZeroPoint[0] = -1
+    INFO - Model OUTPUT tensors: 
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 96 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:   1
+    INFO -          2:  96
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.007843
+    INFO - ZeroPoint[0] = -1
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 22 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:   1
+    INFO -          2:  22
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.003906
+    INFO - ZeroPoint[0] = -128
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 48 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:   1
+    INFO -          2:  48
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.047942
+    INFO - ZeroPoint[0] = -128
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 24 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:   1
+    INFO -          2:  24
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.007843
+    INFO - ZeroPoint[0] = -1
+    INFO -  tensor type is INT8
+    INFO -  tensor occupies 1 bytes with dimensions
+    INFO -          0:   1
+    INFO -          1:   1
+    INFO -          2:   1
+    INFO - Quant dimension: 0
+    INFO - Scale[0] = 0.003906
+    INFO - ZeroPoint[0] = -128
+    INFO - Activation buffer (a.k.a tensor arena) size used: 1940
+    INFO - Number of operators: 1
+    INFO -  Operator 0: ethos-u
+    INFO - Use of Arm uNPU is enabled
+    ```
+
+5. “List audio clips”: Prints a list of pair audio indexes. The original filenames are embedded in
+    the application. For example:
+
+    ```log
+    INFO - List of Files:
+    INFO -  0 => p232_113.wav
+    INFO -  1 => p232_208.wav
+    INFO -  2 => p257_031.wav
+    ```
+
+### Running Noise Reduction
+
+Selecting the first option runs inference on the first file.
+
+The following example illustrates an application output:
+
+```log
+INFO - Audio Clip dump header info (20 bytes) written to 0x80000000
+INFO - Inference 1/136
+INFO - Copied 960 bytes to 0x80000014
+INFO - Inference 2/136
+INFO - Copied 960 bytes to 0x800003d4
+...
+INFO - Inference 136/136
+INFO - Copied 960 bytes to 0x8001fa54
+INFO - Output memory dump of 130580 bytes written at address 0x80000000
+INFO - Final results:
+INFO - Profile for Inference:
+INFO - NPU AXI0_RD_DATA_BEAT_RECEIVED beats: 530 
+INFO - NPU AXI0_WR_DATA_BEAT_WRITTEN beats: 376
+INFO - NPU AXI1_RD_DATA_BEAT_RECEIVED beats: 13911
+INFO - NPU ACTIVE cycles: 103870
+INFO - NPU IDLE cycles: 643
+INFO - NPU TOTAL cycles: 104514
+```
+
+> **Note:** When running Fast Model, each inference can take several seconds on most systems.
+
+Each inference dumps the post processed output to memory. For further information, please refer to: 
+[Dumping post processed results for all inferences](#dumping-post_processed-results-for-all-inferences).
+
+The profiling section of the log shows that for this inference:
+
+- *Ethos-U* NPU PMU report for each inference:
+
+  - 104514: The total number of NPU cycles.
+
+  - 103870: How many NPU cycles were used for computation.
+
+  - 643: How many cycles the NPU was idle for.
+
+  - 530: The number of AXI beats with read transactions from AXI0 bus.
+    > **Note:** The AXI0 is the bus where the *Ethos-U* NPU reads and writes to the computation
+    > buffers, or the activation buf or tensor arenas.
+
+  - 370: The number of AXI beats with write transactions to the AXI0 bus.
+
+  - 13911: The number of AXI beats with read transactions from AXI1 bus.
+    > **Note:** The AXI1 is the bus where *Ethos-U* NPU reads the model, which is read-only.
+
+- For FPGA platforms, the CPU cycle count can also be enabled. However, for FVP, do not use the CPU
+  cycle counters as the CPU model is not cycle-approximate or cycle-accurate.