Difference between revisions of "AI:X-CUBE-AI documentation"

[quality revision] [quality revision]
m (Undo revision 20069 by Vincent Abriou (talk))
(Tag: Undo)
m
 

This article describes the documentation related to X-CUBE-AI and especially the X-CUBE-AI embedded documentation contents and how to access it. The embedded documentation is installed with X-CUBE-AI, which ensures to provide the accurate documentation for the considered version of X-CUBE-AI.

Info white.png Information
  • X-CUBE-AI is a software that generates optimized C code for STM32 microcontrollers and Neural Network inference. It is delivered under the Mix Ultimate Liberty+OSS+3rd-party V1 software license agreement (SLA0048).

1 X-CUBE-AI getting started and user guide[edit]

There are four main documentation items for X-CUBE-AI completed by WiKi articles:

2 X-CUBE-AI embedded documentation contents[edit]

The embedded documentation describes the following topics (for X-CUBE-AI version 7.12.0) in detail:

  • User guide
    • Installation: specific environment settings to use the command-line interface in a console.
    • API Breaking change (NEW): the v7.1.0 introduced a new feature: the multi-heap support which allows to split the activations buffer onto multiple memory segments. Therefore the initialization sequence to instantiate a c-model has been modified to be able to set the address of different activations buffers. The new API reflects this change as well as providing new helper functions.
    • Command-line interface: the stm32ai application is a console utility, which provides a complete and unified command-line interface (CLI) to generate the X-CUBE-AI optimized library for an STM32 device family from a pre-trained DL/ML model. This section describes the CLI in detail.
    • Embedded inference client API: this section describes the embedded inference client API, which must be used by a C application layer (AI client) to use a deployed C model.
    • Evaluation report and metrics: this section describes the different metrics (and associated computing flow) that are used to evaluate the performance of the generated C files (or C model), mainly through the validate command.
    • Quantized model and quantize command: the X-CUBE-AI code generator can be used to deploy a quantized model (8-bit integer format). Quantization (also called calibration) is an optimization technique to compress a 32-bit floating-point model by reducing its size, by improving the CPU/MCU usage and latency, at the expense of a small degradation of the accuracy. This section describes the way X-CUBE-AI supports quantized models and the CLI internal post-training quantization process.
  • Advanced features
    • Relocatable binary network support: a relocatable binary model designates a binary object, which can be installed and executed anywhere in an STM32 memory sub-system. It contains a compiled version of the generated NN C files including the requested forward kernel functions and the weights. The principal objective is to provide a flexible way to upgrade an AI-based application without generating and programming the whole end-user firmware again. For example, this is the primary element needed to use the FOTA (Firmware Over-The-Air) technology. This section describes how to build and use a relocatable binary.
    • Keras stateful LSTM/GRU support: this section describes how X-CUBE-AI provides an initial support for the Keras stateful LSTM.
    • Keras Lambda/custom layer support: the goal of this section is to explain how to import a Keras model containing Lambda or custom layers, following one way or another depending on the nature of the model.
    • Platform Observer API: for advanced run-time, debug or profiling purposes, an AI client application can register a call-back function to be notified before or/end after the execution of a C node. The call-back can be used to measure the execution time, dump the intermediate values, or both. This section describes how to use and take advantage of this feature.
    • STM32 CRC IP as shared resources: to use the network_runtime library, the STM32 CRC IP must be enabled (or clocked); otherwise, the application hangs. To improve the usage of the CRC IP and consider it as a shared resource, two optional specific hooks or call-back functions are defined to facilitate its usage with a resource manager. This section describes how it works.
    • TensorFlow™ Lite for Microcontrollers support: the X-CUBE-AI Expansion Package integrates a specific path, which allows to generate a ready-to-use STM32 IDE project embedding a TensorFlow™ Lite for Microcontrollers runtime and its associated TFLite (TensorFlow™ Lite) model. This can be considered as an alternative to the default X-CUBE-AI solution to deploy an AI solution based on a TFLite model. This section describes how X-CUBE-AI supports TensorFlow™ Lite for Microcontrollers.
  • How to
    • How to use USB-CDC driver for validation: this article explains how to enable the USB-CDC profile to perform faster the validation on the board. A client USB device with the STM32 Communication Device Class (namely the Virtual COM port) is used as communication link with the host. It avoids the overhead of the ST-LINK bridge connecting the UART pins to or from the ST-LINK USB port. However, an STM32 Nucleo or Discovery board with a built-in USB device peripheral is requested.
    • How to run locally a C model: this article explains how to run locally the generated C model. The first goal is to enhance an end-user validation process with a large data set including the specific pre- and post-processing functions with the X-CUBE-AI inference run-time. It is also useful to integrate an X-CUBE-AI validation step in a CI/CD/MLOps flow without an STM32 board.
    • How to upgrade an STM32 project: this article describes how to upgrade manually or with the CLI an STM32CubeMX-based or proprietary source tree with a new version of the X-CUBE-AI library.
  • Supported DL/ML frameworks: lists the supported Deep Learning frameworks, and the operators and layers supported for each of them.
    • Keras toolbox: lists the Keras layers (or operators) that can be imported and converted. Keras is supported through the TensorFlow™ backend with channels-last dimension ordering. Keras.io 2.0 up to version 2.5.1 is supported, while networks defined in Keras 1.x are not officially supported. Up to TF Keras 2.57.0 is supported.
    • TensorFlow™ Lite toolbox: lists the TensorFlow™ Lite layers (or operators) that can be imported and converted. TensorFlow™ Lite is the format used to deploy a Neural Network model on mobile platforms. STM32Cube.AI imports and converts the .tflite files based on the flatbuffer technology. The official ‘schema.fbs’ definition (tags v2.5.0) is used to import the models. A number of operators from the supported operator list are handled, including the quantized models and/or operators generated by the Quantization Aware Training process, by the post-training quantization process, or both.
    • ONNX toolbox: lists the ONNX layers (or operators) that can be imported and converted. ONNX is an open format built to represent Machine Learning models. A subset of operators from Opset 7, 8, 9 and , 10 up to 13 of ONNX 1.6 10 is supported.
    • Machine Learning support (ONNX-ML operators) (NEW): Machine Learning algorithms from Scikit-learn framework or XGBoost package are supported thanks to ONNX representation. After training step, the algorithms should be converted in ONNX format to be deployed and imported. This section details the supported operators and algorithms.
    • Machine Deep Quantized Neural Network support (NEW): The stm32ai application can be used to deploy a pre-trained Deep Quantized Neural Network (DQNN) model. designed and trained with the QKeras and Larq libraries. The purpose of this section is to highlight the supported configurations and limitations to be able to deploy an efficient and optimized c-inference model for STM32 targets.
  • Frequently asked questions
    • Generic aspects
      • How to know the version of the Deep Learning framework components used?
      • Channel first support for ONNX model
      • How is used the CMSIS-NN library?
      • What is the EABI used for the network_runtime libraries?
      • Is it possible to use the AI stack in a C++ environment?
      • X-CUBE-AI Python™ API availability?
      • Requested size of the input buffer is not aligned with the shape and data-type of the tensor?
      • Stateful LSTM/GRU support?
      • How is used the ONNX optimizer?
      • How is used the TFLite interpreter?
      • TensorFlow™ Keras (tf.keras) vs. Keras.io
      • It is possible to update a model on the firmware without having to do a full firmware update?
      • Keras model or sequential layer support?
      • Is it possible to split the weights buffer?
      • Is it possible to place the “activations” buffer in different memory segments?
      • How to compress the non-dense/fully-connected layers?
      • Is it possible to apply a compression factor different from x8, x4?
      • How to specify or indicate a compression factor by layer?
      • Why is a small negative ratio reported for the weights size with a model without compression?
      • Is it possible to dump/capture the intermediate values during inference execution?
    • Validation aspects
      • Validation on target vs. validation on desktop
      • How to interpret the validation results?
      • How to generate npz/npy files from an image data set?
      • How to validate a specific network when multiple networks are embedded into the same firmware?
      • Reported STM32 results are incoherent
      • Unable to perform automatic validation on target
      • Long time process or crash with a large test data set
    • Quantization and post-training quantization process
      • Backward compatibility with X-CUBE-AI 4.0 and X-CUBE-AI 4.1
      • Is it possible to use the Keras post-training quantization process through the UI?
      • Is it possible to use the Keras post-training quantization process with a non-classifier model?
      • Is it possible to use the compression for a quantized model?
      • How to apply the Keras post-training quantization process on a non-Keras model?
      • TensorFlow™ Lite, OPTIMIZE_FOR_SIZE option support

3 X-CUBE-AI embedded documentation access[edit]

To access the embedded documentation, first install X-CUBE-AI. The installation process is described in the getting started user manual. Once the installation is done, the documentation is available in the installation directory under X-CUBE-AI/7.12.0/Documentation/index.html (adapt the example, given here for version 7.12.0, to the version used). For Windows®, by default, the documentation is located here (replace the string username by your Windows® username): file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.12.0/Documentation/index.html. The release notes are available here: file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.12.0/Release_Notes.html.

The embedded documentation can also be accessed through the STM32CubeMX UI, once the X-CUBE-AI Expansion Package has been selected and loaded, by clicking on the "Help" menu and then on "X-CUBE-AI documentation":

X-CUBE-AI documenation


This article describes the documentation related to [https://www.st.com/en/product/x-cube-ai X-CUBE-AI] and especially the [https://www.st.com/en/product/x-cube-ai X-CUBE-AI] embedded documentation contents and how to access it. 
The embedded documentation is installed with [https://www.st.com/en/product/x-cube-ai X-CUBE-AI], which ensures to provide the accurate documentation for the considered version of [https://www.st.com/en/product/x-cube-ai X-CUBE-AI].

{{Info| 
* [https://www.st.com/en/embedded-software/x-cube-ai.html X-CUBE-AI] is a software that generates optimized C code for STM32 microcontrollers and Neural Network inference. It is delivered under the Mix Ultimate Liberty+OSS+3rd-party V1 software license agreement ([https://st.com/SLA0048 SLA0048]).}}

= X-CUBE-AI getting started and user guide =

There are four main documentation items for [https://www.st.com/en/product/x-cube-ai X-CUBE-AI] completed by WiKi articles:
* Documentation available on [https://www.st.com/en/product/x-cube-ai#documentation ''st.com'']:
** [https://www.st.com/resource/en/data_brief/x-cube-ai.pdf X-CUBE-AI data brief]
** [https://www.st.com/resource/en/user_manual/dm00570145.pdf X-CUBE-AI getting started user manual]
* The release notes for each version available within [https://www.st.com/en/product/x-cube-ai X-CUBE-AI]
* Embedded documentation available within the [https://www.st.com/en/product/x-cube-ai X-CUBE-AI] Expansion Package and described below.

= X-CUBE-AI embedded documentation contents =

The embedded documentation describes the following topics (for [https://www.st.com/en/product/x-cube-ai X-CUBE-AI]  version '''7.12.0''') in detail:
* {{Highlight|User guide}}
** {{Highlight|Installation}}: specific environment settings to use the command-line interface in a console.
** {{Highlight|API Breaking change (NEW)}}: the v7.1.0 introduced a new feature: the multi-heap support which allows to split the activations buffer onto multiple memory segments. Therefore the initialization sequence to instantiate a c-model has been modified to be able to set the address of different activations buffers. The new API reflects this change as well as providing new helper functions.
** {{Highlight|Command-line interface}}: the stm32ai application is a console utility, which provides a complete and unified command-line interface (CLI) to generate the X-CUBE-AI optimized library for an STM32 device family from a pre-trained DL/ML model. This section describes the CLI in detail.
** {{Highlight|Embedded inference client API}}: this section describes the embedded inference client API, which must be used by a C application layer (AI client) to use a deployed C model.
** {{Highlight|Evaluation report and metrics}}: this section describes the different metrics (and associated computing flow) that are used to evaluate the performance of the generated C files (or C model), mainly through the validate command.
** {{Highlight|Quantized model and quantize command}}: the X-CUBE-AI code generator can be used to deploy a quantized model (8-bit integer format). Quantization (also called calibration) is an optimization technique to compress a 32-bit floating-point model by reducing its size, by improving the CPU/MCU usage and latency, at the expense of a small degradation of the accuracy. This section describes the way X-CUBE-AI supports quantized models and the CLI internal post-training quantization process.
* {{Highlight|Advanced features}}
** {{Highlight|Relocatable binary network support}}: a relocatable binary model designates a binary object, which can be installed and executed anywhere in an STM32 memory sub-system. It contains a compiled version of the generated NN C files including the requested forward kernel functions and the weights. The principal objective is to provide a flexible way to upgrade an AI-based application without generating and programming the whole end-user firmware again. For example, this is the primary element needed to use the FOTA (Firmware Over-The-Air) technology. This section describes how to build and use a relocatable binary.
** {{Highlight|Keras stateful LSTM/GRU support}}: this section describes how X-CUBE-AI provides an initial support for the Keras stateful LSTM. 
** {{Highlight|Keras Lambda/custom layer support}}: the goal of this section is to explain how to import a Keras model containing Lambda or custom layers, following one way or another depending on the nature of the model.
** {{Highlight|Platform Observer API}}: for advanced run-time, debug or profiling purposes, an AI client application can register a call-back function to be notified before or/end after the execution of a C node. The call-back can be used to measure the execution time, dump the intermediate values, or both. This section describes how to use and take advantage of this feature.
** {{Highlight|STM32 CRC IP as shared resources}}: to use the network_runtime library, the STM32 CRC IP must be enabled (or clocked); otherwise, the application hangs. To improve the usage of the CRC IP and consider it as a shared resource, two optional specific hooks or call-back functions are defined to facilitate its usage with a resource manager. This section describes how it works.
** {{Highlight|TensorFlow™ Lite for Microcontrollers support}}: the X-CUBE-AI Expansion Package integrates a specific path, which allows to generate a ready-to-use STM32 IDE project embedding a TensorFlow™ Lite for Microcontrollers runtime and its associated TFLite (TensorFlow™ Lite) model. This can be considered as an alternative to the default X-CUBE-AI solution to deploy an AI solution based on a TFLite model. This section describes how X-CUBE-AI supports TensorFlow™ Lite for Microcontrollers.
* {{Highlight|How to}}
** {{Highlight|How to use USB-CDC driver for validation}}: this article explains how to enable the USB-CDC profile to perform faster the validation on the board. A client USB device with the STM32 Communication Device Class (namely the Virtual COM port) is used as communication link with the host. It avoids the overhead of the ST-LINK bridge connecting the UART pins to or from the ST-LINK USB port. However, an STM32 Nucleo or Discovery board with a built-in USB device peripheral is requested.
** {{Highlight|How to run locally a C model}}: this article explains how to run locally the generated C model. The first  goal is to enhance an end-user validation process with a large data set including the specific pre- and post-processing functions with the X-CUBE-AI inference run-time. It is also useful to integrate an X-CUBE-AI validation step in a CI/CD/MLOps flow without an STM32 board.
** {{Highlight|How to upgrade an STM32 project}}: this article describes how to upgrade manually or with the CLI an [https://www.st.com/en/product/stm32cubemx STM32CubeMX]-based or proprietary source tree with a new version of the X-CUBE-AI library. 
* {{Highlight|Supported DL/ML frameworks}}: lists the supported Deep Learning frameworks, and the operators and layers supported for each of them.
** {{Highlight|Keras toolbox}}: lists the Keras layers (or operators) that can be imported and converted. Keras is supported through the TensorFlow™ backend with channels-last dimension ordering. Keras.io 2.0 up to version 2.5.1 is supported, while networks defined in Keras 1.x are not officially supported. Up to TF Keras 2.57.0 is supported.
** {{Highlight|TensorFlow™ Lite toolbox}}: lists the TensorFlow™ Lite layers (or operators) that can be imported and converted. TensorFlow™ Lite is the format used to deploy a Neural Network model on mobile platforms. STM32Cube.AI imports and converts the .tflite files based on the flatbuffer technology. The official ‘schema.fbs’ definition (tags v2.5.0) is used to import the models. A number of operators from the supported operator list are handled, including the quantized models and/or operators generated by the Quantization Aware Training process, by the post-training quantization process, or both.
** {{Highlight|ONNX toolbox}}: lists the ONNX layers (or operators) that can be imported and converted. ONNX is an open format built to represent Machine Learning models. A subset of operators from Opset 7, 8, 9 and , 10 up to 13 of ONNX 1.610 is supported.
** {{Highlight|Machine Learning support (ONNX-ML operators) (NEW)}}: Machine Learning algorithms from Scikit-learn framework or XGBoost package are supported thanks to ONNX representation. After training step, the algorithms should be converted in ONNX format to be deployed and imported. This section details the supported operators and algorithms. 
** {{Highlight|Machine Deep Quantized Neural Network support (NEW)}}: The stm32ai application can be used to deploy a pre-trained Deep Quantized Neural Network (DQNN) model. designed and trained with the QKeras and Larq libraries. The purpose of this section is to highlight the supported configurations and limitations to be able to deploy an efficient and optimized c-inference model for STM32 targets. 
* {{Highlight|Frequently asked questions}}
** {{Highlight|Generic aspects}}
*** How to know the version of the Deep Learning framework components used?
*** Channel first support for ONNX model
*** How is used the CMSIS-NN library?
*** What is the EABI used for the network_runtime libraries?
*** Is it possible to use the AI stack in a C++ environment?
*** X-CUBE-AI Python™ API availability?
*** Requested size of the input buffer is not aligned with the shape and data-type of the tensor?
*** Stateful LSTM/GRU support?
*** How is used the ONNX optimizer?
*** How is used the TFLite interpreter?
*** TensorFlow™ Keras (tf.keras) vs. Keras.io
*** It is possible to update a model on the firmware without having to do a full firmware update?
*** Keras model or sequential layer support?
*** Is it possible to split the weights buffer?
*** Is it possible to place the “activations” buffer in different memory segments?
*** How to compress the non-dense/fully-connected layers?
*** Is it possible to apply a compression factor different from x8, x4?
*** How to specify or indicate a compression factor by layer?
*** Why is a small negative ratio reported for the weights size with a model without compression?
*** Is it possible to dump/capture the intermediate values during inference execution?
** {{Highlight|Validation aspects}}
*** Validation on target vs. validation on desktop
*** How to interpret the validation results?
*** How to generate npz/npy files from an image data set?
*** How to validate a specific network when multiple networks are embedded into the same firmware?
*** Reported STM32 results are incoherent
*** Unable to perform automatic validation on target
*** Long time process or crash with a large test data set
** {{Highlight|Quantization and post-training quantization process}}
*** Backward compatibility with X-CUBE-AI 4.0 and X-CUBE-AI 4.1
*** Is it possible to use the Keras post-training quantization process through the UI?
*** Is it possible to use the Keras post-training quantization process with a non-classifier model?
*** Is it possible to use the compression for a quantized model?
*** How to apply the Keras post-training quantization process on a non-Keras model?
*** TensorFlow™ Lite, OPTIMIZE_FOR_SIZE option support

= X-CUBE-AI embedded documentation access =

To access the embedded documentation, first install [https://www.st.com/en/product/x-cube-ai X-CUBE-AI].
The installation process is described in the [https://www.st.com/resource/en/user_manual/dm00570145.pdf getting started] user manual.
Once the installation is done, the documentation is available in the installation directory under X-CUBE-AI/7.12.0/Documentation/index.html (adapt the example, given here for version 7.12.0, to the version used).
For Windows<sup>®</sup>, by default, the documentation is located here (replace the string ''username'' by your Windows<sup>®</sup> username): file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.12.0/Documentation/index.html.
The release notes are available here: file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.12.0/Release_Notes.html.

The embedded documentation can also be accessed through the [https://www.st.com/en/product/stm32cubemx STM32CubeMX] UI, once the X-CUBE-AI Expansion Package has been selected and loaded, by clicking on the "Help" menu and then on "X-CUBE-AI documentation":
<div class="res-img">

[[File:CubeAI_Help_Doc.png|center|alt=X-CUBE-AI documenation|X-CUBE-AI documentation]]</div>

<noinclude>

[[Category:STM32Cube.AI | 0]]
{{PublicationRequestId | 20964 | 2021-09-01 }}	</noinclude>
Line 16: Line 16:
 
= X-CUBE-AI embedded documentation contents =
 
= X-CUBE-AI embedded documentation contents =
   
The embedded documentation describes the following topics (for [https://www.st.com/en/product/x-cube-ai X-CUBE-AI]  version '''7.1.0''') in detail:
+
The embedded documentation describes the following topics (for [https://www.st.com/en/product/x-cube-ai X-CUBE-AI]  version '''7.2.0''') in detail:
 
* {{Highlight|User guide}}
 
* {{Highlight|User guide}}
 
** {{Highlight|Installation}}: specific environment settings to use the command-line interface in a console.
 
** {{Highlight|Installation}}: specific environment settings to use the command-line interface in a console.
Line 36: Line 36:
 
** {{Highlight|How to upgrade an STM32 project}}: this article describes how to upgrade manually or with the CLI an [https://www.st.com/en/product/stm32cubemx STM32CubeMX]-based or proprietary source tree with a new version of the X-CUBE-AI library.  
 
** {{Highlight|How to upgrade an STM32 project}}: this article describes how to upgrade manually or with the CLI an [https://www.st.com/en/product/stm32cubemx STM32CubeMX]-based or proprietary source tree with a new version of the X-CUBE-AI library.  
 
* {{Highlight|Supported DL/ML frameworks}}: lists the supported Deep Learning frameworks, and the operators and layers supported for each of them.
 
* {{Highlight|Supported DL/ML frameworks}}: lists the supported Deep Learning frameworks, and the operators and layers supported for each of them.
** {{Highlight|Keras toolbox}}: lists the Keras layers (or operators) that can be imported and converted. Keras is supported through the TensorFlow™ backend with channels-last dimension ordering. Keras.io 2.0 up to version 2.5.1 is supported, while networks defined in Keras 1.x are not officially supported. Up to TF Keras 2.5.0 is supported.
+
** {{Highlight|Keras toolbox}}: lists the Keras layers (or operators) that can be imported and converted. Keras is supported through the TensorFlow™ backend with channels-last dimension ordering. Keras.io 2.0 up to version 2.5.1 is supported, while networks defined in Keras 1.x are not officially supported. Up to TF Keras 2.7.0 is supported.
 
** {{Highlight|TensorFlow™ Lite toolbox}}: lists the TensorFlow™ Lite layers (or operators) that can be imported and converted. TensorFlow™ Lite is the format used to deploy a Neural Network model on mobile platforms. STM32Cube.AI imports and converts the .tflite files based on the flatbuffer technology. The official ‘schema.fbs’ definition (tags v2.5.0) is used to import the models. A number of operators from the supported operator list are handled, including the quantized models and/or operators generated by the Quantization Aware Training process, by the post-training quantization process, or both.
 
** {{Highlight|TensorFlow™ Lite toolbox}}: lists the TensorFlow™ Lite layers (or operators) that can be imported and converted. TensorFlow™ Lite is the format used to deploy a Neural Network model on mobile platforms. STM32Cube.AI imports and converts the .tflite files based on the flatbuffer technology. The official ‘schema.fbs’ definition (tags v2.5.0) is used to import the models. A number of operators from the supported operator list are handled, including the quantized models and/or operators generated by the Quantization Aware Training process, by the post-training quantization process, or both.
** {{Highlight|ONNX toolbox}}: lists the ONNX layers (or operators) that can be imported and converted. ONNX is an open format built to represent Machine Learning models. A subset of operators from Opset 7, 8, 9 and 10 of ONNX 1.6 is supported.
+
** {{Highlight|ONNX toolbox}}: lists the ONNX layers (or operators) that can be imported and converted. ONNX is an open format built to represent Machine Learning models. A subset of operators from Opset 7, 8, 9, 10 up to 13 of ONNX 1.10 is supported.
** {{Highlight|Machine Learning support (ONNX-ML operators) (NEW)}}: Machine Learning algorithms from Scikit-learn framework or XGBoost package are supported thanks to ONNX representation. After training step, the algorithms should be converted in ONNX format to be deployed and imported. This section details the supported operators and algorithms.  
+
** {{Highlight|Machine Learning support (ONNX-ML operators)}}: Machine Learning algorithms from Scikit-learn framework or XGBoost package are supported thanks to ONNX representation. After training step, the algorithms should be converted in ONNX format to be deployed and imported. This section details the supported operators and algorithms.
  +
** {{Highlight|Machine Deep Quantized Neural Network support (NEW)}}: The stm32ai application can be used to deploy a pre-trained Deep Quantized Neural Network (DQNN) model. designed and trained with the QKeras and Larq libraries. The purpose of this section is to highlight the supported configurations and limitations to be able to deploy an efficient and optimized c-inference model for STM32 targets.  
 
* {{Highlight|Frequently asked questions}}
 
* {{Highlight|Frequently asked questions}}
 
** {{Highlight|Generic aspects}}
 
** {{Highlight|Generic aspects}}
Line 82: Line 83:
 
To access the embedded documentation, first install [https://www.st.com/en/product/x-cube-ai X-CUBE-AI].
 
To access the embedded documentation, first install [https://www.st.com/en/product/x-cube-ai X-CUBE-AI].
 
The installation process is described in the [https://www.st.com/resource/en/user_manual/dm00570145.pdf getting started] user manual.
 
The installation process is described in the [https://www.st.com/resource/en/user_manual/dm00570145.pdf getting started] user manual.
Once the installation is done, the documentation is available in the installation directory under X-CUBE-AI/7.1.0/Documentation/index.html (adapt the example, given here for version 7.1.0, to the version used).
+
Once the installation is done, the documentation is available in the installation directory under X-CUBE-AI/7.2.0/Documentation/index.html (adapt the example, given here for version 7.2.0, to the version used).
For Windows<sup>®</sup>, by default, the documentation is located here (replace the string ''username'' by your Windows<sup>®</sup> username): file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.1.0/Documentation/index.html.
+
For Windows<sup>®</sup>, by default, the documentation is located here (replace the string ''username'' by your Windows<sup>®</sup> username): file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.2.0/Documentation/index.html.
The release notes are available here: file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.1.0/Release_Notes.html.
+
The release notes are available here: file:///C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.2.0/Release_Notes.html.
   
 
The embedded documentation can also be accessed through the [https://www.st.com/en/product/stm32cubemx STM32CubeMX] UI, once the X-CUBE-AI Expansion Package has been selected and loaded, by clicking on the "Help" menu and then on "X-CUBE-AI documentation":
 
The embedded documentation can also be accessed through the [https://www.st.com/en/product/stm32cubemx STM32CubeMX] UI, once the X-CUBE-AI Expansion Package has been selected and loaded, by clicking on the "Help" menu and then on "X-CUBE-AI documentation":