ModusToolbox™ Machine Learning Middleware Library

Overview

The ModusToolbox™ ML Middleware Library is a set of helper functions to handle the Machine Learning models and capability to receive stream validation data.

Features

• mtb_ml_utils - functions to help handling the inputs/outputs of a ML model
• mtb_ml_model - functions to initialize and inference a ML model
• mtb_ml_stream - functions to stream validation data

Requirements

• ModusToolbox™ software v3.0 or greater
• Programming Language: C
• Associated Parts: See "Supported Kits" section below.

Supported Device Family

• PSoC™ 6

Supported Toolchains (make variable 'TOOLCHAIN')

• GNU Arm® Embedded Compiler 10.3-2021.07 (GCC_ARM)
• Arm compiler v6.16 (ARM)
• IAR C/C++ compiler v9.30.1 (IAR)

The ModusToolbox™ ML Middleware library supports the following operating systems:

• Windows 7 and greater
• Ubuntu 18.04 and greater
• macOS Catalina and greater

Quick Start Guide

This quick start guide assumes that the environment is configured for ModusToolbox™ applications development. If PSoC™ 64 device is being used for development, it is provisioned as documented in PSoC™ 64 Secure MCU Secure Boot SDK User Guide.

Adding the library

Middleware library is provided in the form of the source code. There are two ways to add library to our project:

• add a dependency file (MTB format) into the 'deps' folder;
• use the Library Manager. It is available under Libraries Tab > Machine Learning > ml-middleware.

Using the library - ML inference engines

This library assumes that the selected inference engine has been set up.

The ml-tflite-micro and ml-inference libraries are available as a ModusToolbox™ asset. Use the following GitHub links: https://github.com/infineon/ml-tflite-micro, https://github.com/infineon/ml-inference. You can add a dependency file (mtb format) under the deps folder or use the Library Manager to add it to your application. It is available under Library -> Machine Learning -> ml-tflite-micro/ml-inference.

Refer to the ml-tflite-micro/ml-inference README.md files for further information on the COMPONENTS and DEFINES that must be added in order to select the preferred inference engine.

Using CMSIS DSP

Some of the mtb ml utils functions from the ml-middleware library can use the CMSIS-DSP functions to accelerate its execution. To enable it, add CMSIS_DSP in your makefile COMPONENTS list and MTB_ML_HAVING_CMSIS_DSP must be added to the DEFINES. NOTE: In the next release MTB_ML_HAVING_CMSIS_DSP will be deprecated and mtb ml utils functions will only check that the CMSIS_DSP component has been added.

Using the library - ML stream

1. Make sure the application includes header files and selected model:

#include "mtb_ml_stream.h"
#include "mtb_ml_model.h"
#include MTB_ML_INCLUDE_MODEL_FILE(MODEL_NAME)

2. Initialize the streaming engine:

    mtb_ml_model_bin_t model_bin = {MTB_ML_MODEL_BIN_DATA(MODEL_NAME)};

    mtb_ml_stream_interface_t interface = {CY_ML_INTERFACE_UART, &cy_retarget_io_uart_obj};

    status = mtb_ml_stream_init(&interface, CY_ML_PROFILE_ENABLE_MODEL_PER_FRAME, &model_bin);
    if(status != MTB_ML_RESULT_SUCCESS)
    {
        printf("ERROR: mtb_ml_stream_init returned error, bailing out\r\n");
        return;
    }

Note: currently the streaming API supports only UART port and shares debug port with any debug messages printed by the application. Pass cy_retarget_io_uart_obj created by a call to cy_retarget_io_init() as an interface argument to mtb_ml_stream_init.

3. Call mtb_ml_stream_task() to start the streaming process. On the desktop side start the streaming application ml-coretools-streaming.
4. After the streaming test is done and mtb_ml_stream_task() returns, call mtb_ml_stream_deinit() to release any resources.

More information

The following resources contain more information:

• ModusToolbox™ Machine Learning Design Support
• ModusToolbox™ Machine Learning Middleware Library RELEASE.md
• MTB ML Middleware API Reference Guide
• PSoC™ 6
• Cypress Semiconductor, an Infineon Technologies Company
• Cypress Semiconductor GitHub
• ModusToolbox™

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© 2021-2022, Cypress Semiconductor Corporation (an Infineon company) or an affiliate of Cypress Semiconductor Corporation.