Anomaly Detection Code Sample


This document describes the process of setting up and running the Arm® Ethos™-U55 Anomaly Detection example.

Use case code could be found in source/use_case/ad directory.

Preprocessing and feature extraction

The Anomaly Detection model that is used with the Code Samples expects audio data to be preprocessed in a specific way before performing an inference. This section aims to provide an overview of the feature extraction process used.

First the audio data is normalized to the range (-1, 1).

Next, a window of 1024 audio samples are taken from the start of the audio clip. From these 1024 samples we calculate 64 Log Mel Energies that form part of a Log Mel Spectrogram.

The window is shifted by 512 audio samples and another 64 Log Mel Energies are calculated. This is repeated until we have 64 sets of Log Mel Energies.

This 64x64 matrix of values is resized by a factor of 2 resulting in a 32x32 matrix of values.

The average of the training dataset is subtracted from this 32x32 matrix and an inference can then be performed.

We start this process again but shifting the start by 20*512=10240 audio samples. This keeps repeating until enough inferences have been performed to cover the whole audio clip.


Softmax is applied to the result of each inference. Based on the machine ID of the wav clip being processed we look at a specific index in each output vector. An average of the negative value at this index across all the inferences performed for the audio clip is taken. If this average value is greater than a chosen threshold score, then the machine in the clip is not behaving anomalously. If the score is lower than the threshold then the machine in the clip is behaving anomalously.


See Prerequisites

Building the code sample application from sources

Build options

In addition to the already specified build option in the main documentation, Anomaly Detection use case adds:

  • ad_MODEL_TFLITE_PATH - Path to the NN model file in TFLite format. Model will be processed and included into the application axf file. The default value points to one of the delivered set of models. Note that the parameters ad_LABELS_TXT_FILE, TARGET_PLATFORM and ETHOS_U55_ENABLED should be aligned with the chosen model, i.e.:

    • if ETHOS_U55_ENABLED is set to On or 1, the NN model is assumed to be optimized. The model will naturally fall back to the Arm® Cortex®-M CPU if an unoptimized model is supplied.
    • if ETHOS_U55_ENABLED is set to Off or 0, the NN model is assumed to be unoptimized. Supplying an optimized model in this case will result in a runtime error.
  • ad_FILE_PATH: Path to the directory containing audio files, or a path to single WAV file, to be used in the application. The default value points to the resources/ad/samples folder containing the delivered set of audio clips.

  • ad_AUDIO_RATE: Input data sampling rate. Each audio file from ad_FILE_PATH is preprocessed during the build to match NN model input requirements. Default value is 16000.

  • ad_AUDIO_MONO: If set to ON the audio data will be converted to mono. Default is ON.

  • ad_AUDIO_OFFSET: Start loading audio data starting from this offset (in seconds). Default value is 0.

  • ad_AUDIO_DURATION: Length of the audio data to be used in the application in seconds. Default is 0 meaning the whole audio file will be taken.

  • ad_AUDIO_MIN_SAMPLES: Minimum number of samples required by the network model. If the audio clip is shorter than this number, it is padded with zeros. Default value is 16000.

  • ad_MODEL_SCORE_THRESHOLD: Threshold value to be applied to average softmax score over the clip, if larger than this score we have an anomaly.

  • ad_ACTIVATION_BUF_SZ: The intermediate/activation buffer size reserved for the NN model. By default, it is set to 2MiB and should be enough for most models.

In order to build ONLY Anomaly Detection example application add to the cmake command line specified in Building -DUSE_CASE_BUILD=ad.

Build process

Note: This section describes the process for configuring the build for MPS3: SSE-300 for different target platform see Building.

Create a build directory folder and navigate inside:

mkdir build_ad && cd build_ad

On Linux, execute the following command to build only Anomaly Detection application to run on the Ethos-U55 Fast Model when providing only the mandatory arguments for CMake configuration:

cmake ../ -DUSE_CASE_BUILD=ad

To configure a build that can be debugged using Arm-DS, we can just specify the build type as Debug and use the Arm Compiler toolchain file:

cmake .. \
    -DCMAKE_TOOLCHAIN_FILE=scripts/cmake/toolchains/bare-metal-armclang.cmake \

Also see:

Note: If re-building with changed parameters values, it is highly advised to clean the build directory and re-run the CMake command.

If the CMake command succeeded, build the application as follows:

make -j4

Add VERBOSE=1 to see compilation and link details.

Results of the build will be placed under build/bin folder:

 ├── ethos-u-.axf
 ├── ethos-u-ad.htm
 └── sectors
      ├── images.txt
      └── ad
          ├── dram.bin
          └── itcm.bin


  • ethos-u-ad.axf: The built application binary for the Anomaly Detection use case.

  • Information from building the application (e.g. libraries used, what was optimized, location of objects)

  • ethos-u-ad.htm: Human readable file containing the call graph of application functions.

  • sectors/ad: Folder containing the built application, split into files for loading into different FPGA memory regions.

  • sectors/images.txt: Tells the FPGA which memory regions to use for loading the binaries in sectors/** folder.

Add custom input

The application anomaly detection on audio data found in the folder, or an individual file, set by the CMake parameter ad_FILE_PATH.

To run the application with your own audio clips first create a folder to hold them and then copy the custom clips into this folder:

mkdir /tmp/custom_files

cp custom_id_00.wav /tmp/custom_files/

Note: The data used for this example comes from and the model included in this example is trained on the ‘Slider’ part of the dataset. The machine ID (00, 02, 04, 06) the clip comes from must be in the file name for the application to work. The file name should have a pattern that matches e.g. <any>_<text>_00_<here>.wav if the audio was from machine ID 00 or <any>_<text>_02_<here>.wav if it was from machine ID 02 etc.

Note: Clean the build directory before re-running the CMake command.

Next set ad_FILE_PATH to the location of this folder when building:

cmake .. \
    -Dad_FILE_PATH=/tmp/custom_files/ \

The audio flies found in the ad_FILE_PATH folder will be picked up and automatically converted to C++ files during the CMake configuration stage and then compiled into the application during the build phase for performing inference with.

The log from the configuration stage should tell you what image directory path has been used:

-- User option ad_FILE_PATH is set to /tmp/custom_files

After compiling, your custom inputs will have now replaced the default ones in the application.

Add custom model

The application performs inference using the model pointed to by the CMake parameter ad_MODEL_TFLITE_PATH.

Note: If you want to run the model using Ethos-U55, ensure your custom model has been run through the Vela compiler successfully before continuing. See Optimize model with Vela compiler.

An example:

cmake .. \
    -Dad_MODEL_TFLITE_PATH=<path/to/custom_ad_model_after_vela.tflite> \

Note: Clean the build directory before re-running the CMake command.

The .tflite model file pointed to by ad_MODEL_TFLITE_PATH will be converted to C++ files during the CMake configuration stage and then compiled into the application for performing inference with.

The log from the configuration stage should tell you what model path has been used:

-- User option TARGET_PLATFORM is set to fastmodel
-- User option ad_MODEL_TFLITE_PATH is set to <path/to/custom_ad_model_after_vela.tflite>
-- Using <path/to/custom_ad_model_after_vela.tflite>
++ Converting custom_ad_model_after_vela.tflite to

After compiling, your custom model will have now replaced the default one in the application.

Note: In order to successfully run the model, the NPU needs to be enabled and the platform TARGET_PLATFORM is set to mps3 and TARGET_SUBSYSTEM is SSE-300.

Setting-up and running Ethos-U55 Code Sample

Setting up the Ethos-U55 Fast Model

The FVP is available publicly from Arm Ecosystem FVP downloads .

For Ethos-U55 evaluation, please download the MPS3 version of the Arm® Corstone™-300 model that contains Ethos-U55 and Cortex-M55. The model is currently only supported on Linux based machines. To install the FVP:

  • Unpack the archive

  • Run the install script in the extracted package
  • Follow the instructions to install the FVP to your desired location

Starting Fast Model simulation

Note: The anomaly detection example does not come pre-built. You will first need to follow the instructions in section 3 for building the application from source.

After building, and assuming the install location of the FVP was set to ~/FVP_install_location, the simulation can be started by:

~/FVP_install_location/models/Linux64_GCC-6.4/FVP_Corstone_SSE-300_Ethos-U55 ./bin/ethos-u-ad.axf

A log output should appear on the terminal:

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 will also launch a telnet window with the sample application's standard output and error log entries containing information about the pre-built application version, TensorFlow Lite Micro library version used, data type as well as the input and output tensor sizes of the model compiled into the executable binary.

After the application has started if ad_FILE_PATH pointed to a single file (or a folder containing a single input file) the inference starts immediately. In case of multiple inputs choice, it outputs a menu and waits for the user input from telnet terminal:

User input required
Enter option number from:

1. Classify next audio clip
2. Classify audio clip at chosen index
3. Run classification on all audio clips
4. Show NN model info
5. List audio clips


  1. “Classify next audio clip” menu option will run single inference on the next in line.

  2. “Classify audio clip at chosen index” menu option will run inference on the chosen audio clip.

    Note: Please make sure to select audio clip index in the range of supplied audio clips during application build. By default, pre-built application has 4 files, indexes from 0 to 3.

  3. “Run ... on all” menu option triggers sequential inference executions on all built-in .

  4. “Show NN model info” menu option prints information about model data type, input and output tensor sizes:

    INFO - uTFL version: 2.5.0
    INFO - Model info:
    INFO - Model INPUT tensors:
    INFO - 	tensor type is INT8
    INFO - 	tensor occupies 1024 bytes with dimensions
    INFO - 		0:   1
    INFO - 		1:  32
    INFO - 		2:  32
    INFO - 		3:   1
    INFO - Quant dimension: 0
    INFO - Scale[0] = 0.192437
    INFO - ZeroPoint[0] = 11
    INFO - Model OUTPUT tensors:
    INFO - 	tensor type is INT8
    INFO - 	tensor occupies 8 bytes with dimensions
    INFO - 		0:   1
    INFO - 		1:   8
    INFO - Quant dimension: 0
    INFO - Scale[0] = 0.048891
    INFO - ZeroPoint[0] = -30
    INFO - Activation buffer (a.k.a tensor arena) size used: 198016
    INFO - Number of operators: 1
    INFO - 	Operator 0: ethos-u
  5. “List” menu option prints a list of pair ... indexes - the original filenames embedded in the application:

    INFO - List of Files:
    INFO - 0 =>; anomaly_id_00_00000000.wav
    INFO - 1 =>; anomaly_id_02_00000076.wav
    INFO - 2 =>; normal_id_00_00000004.wav
    INFO - 3 =>; normal_id_02_00000001.wav

Running Anomaly Detection

Please select the first menu option to execute Anomaly Detection.

The following example illustrates application output:

INFO - Running inference on audio clip 0 => anomaly_id_00_00000000.wav
INFO - Inference 1/13
INFO - Inference 2/13
INFO - Inference 3/13
INFO - Inference 4/13
INFO - Inference 5/13
INFO - Inference 6/13
INFO - Inference 7/13
INFO - Inference 8/13
INFO - Inference 9/13
INFO - Inference 10/13
INFO - Inference 11/13
INFO - Inference 12/13
INFO - Inference 13/13
INFO - Average anomaly score is: -0.024493
Anomaly threshold is: -0.800000
Anomaly detected!
INFO - Profile for Inference:
INFO - NPU ACTIVE cycles: 1081007
INFO - NPU IDLE cycles: 626
INFO - NPU total cycles: 1081634

As multiple inferences have to be run for one clip it will take around a minute or so for all inferences to complete.

For the anomaly_id_00_00000000.wav clip, after averaging results across all inferences the score is greater than the chosen anomaly threshold so an anomaly was detected with the machine in this clip.

The profiling section of the log shows that for each inference. For the last inference the profiling reports:

  • Ethos-U55's PMU report:

    • 1,081,634 total cycle: The number of NPU cycles

    • 1,081,007 active cycles: number of NPU cycles that were used for computation

    • 626 idle cycles: number of cycles for which the NPU was idle

    • 628,122 AXI0 read beats: The number of AXI beats with read transactions from AXI0 bus. AXI0 is the bus where Ethos-U55 NPU reads and writes to the computation buffers (activation buf/tensor arenas).

    • 135,087 AXI0 write beats: The number of AXI beats with write transactions to AXI0 bus.

    • 62,870 AXI1 read beats: The number of AXI beats with read transactions from AXI1 bus. AXI1 is the bus where Ethos-U55 NPU reads the model (read only)

  • For FPGA platforms, CPU cycle count can also be enabled. For FVP, however, CPU cycle counters should not be used as the CPU model is not cycle-approximate or cycle-accurate.