Selecting the Best Framework for Embedded Machine Learning

Learn how to select the best framework for embedded machine learning by comparing the features and benefits of TensorFlow Lite, PyTorch Mobile, Edge Impulse, and MicroPython.

Table of Contents

1. Introduction

Embedded machine learning is the process of running machine learning models on devices with limited resources, such as microcontrollers, sensors, or edge devices. Embedded machine learning enables applications such as smart home devices, wearable health monitors, industrial automation, and autonomous vehicles.

However, developing and deploying embedded machine learning applications is not a trivial task. You need to consider various factors, such as the hardware specifications, the software libraries, the model size, the inference speed, the power consumption, and the security of your device. You also need to optimize your machine learning model for the specific constraints and requirements of your target device.

Fortunately, there are several frameworks that can help you with embedded machine learning development. Frameworks are software tools that provide a set of features and functionalities to simplify and streamline the development process. Frameworks can help you with tasks such as data collection, model training, model conversion, model deployment, model testing, and model monitoring.

In this blog, you will learn about some of the most popular frameworks for embedded machine learning, such as TensorFlow Lite, PyTorch Mobile, Edge Impulse, and MicroPython. You will compare their features and benefits, and see how they can help you create amazing embedded machine learning applications. You will also learn how to choose the best framework for your project, based on your goals and needs.

Are you ready to dive into the world of embedded machine learning frameworks? Let’s get started!

2. What is Embedded Machine Learning?

In this section, you will learn what embedded machine learning is, how it differs from traditional machine learning, and what are the main challenges and opportunities of embedded machine learning. You will also learn some of the key terms and concepts related to embedded machine learning, such as edge computing, tinyML, and model optimization.

3. Why Use a Framework for Embedded Machine Learning?

As you have learned in the previous section, embedded machine learning is the process of running machine learning models on devices with limited resources, such as microcontrollers, sensors, or edge devices. Embedded machine learning enables applications such as smart home devices, wearable health monitors, industrial automation, and autonomous vehicles.

This is where frameworks come in handy. Frameworks are software tools that provide a set of features and functionalities to simplify and streamline the development process. Frameworks can help you with tasks such as data collection, model training, model conversion, model deployment, model testing, and model monitoring.

By using a framework for embedded machine learning, you can benefit from the following advantages:

Save time and effort: Frameworks can automate many of the tedious and repetitive tasks involved in embedded machine learning development, such as data preprocessing, model compression, code generation, and device integration. This can save you a lot of time and effort, and allow you to focus on the core logic and functionality of your application.
Improve performance and efficiency: Frameworks can optimize your machine learning model for the specific hardware and software environment of your target device, such as the memory, CPU, GPU, OS, and drivers. This can improve the performance and efficiency of your model, such as the inference speed, accuracy, power consumption, and security.
Access state-of-the-art technologies and best practices: Frameworks can provide you with access to the latest technologies and best practices in embedded machine learning, such as the most advanced algorithms, architectures, libraries, and tools. This can help you create more robust, reliable, and scalable applications, and keep up with the fast-evolving field of embedded machine learning.
Leverage a large and active community: Frameworks can connect you with a large and active community of developers, researchers, and users who are working on similar or related projects. You can benefit from the community’s support, feedback, collaboration, and innovation, and contribute to the advancement of the field of embedded machine learning.

As you can see, using a framework for embedded machine learning can make your development process easier, faster, and better. But how do you choose the best framework for your project? There are many frameworks available, each with its own strengths and weaknesses, features and functionalities, pros and cons. In the next section, you will learn about some of the most popular frameworks for embedded machine learning, such as TensorFlow Lite, PyTorch Mobile, Edge Impulse, and MicroPython. You will compare their features and benefits, and see how they can help you create amazing embedded machine learning applications.

4. Comparison of Popular Frameworks for Embedded Machine Learning

In this section, you will learn about some of the most popular frameworks for embedded machine learning, such as TensorFlow Lite, PyTorch Mobile, Edge Impulse, and MicroPython. You will compare their features and benefits, and see how they can help you create amazing embedded machine learning applications.

These frameworks are not the only ones available, but they are among the most widely used and supported ones in the field of embedded machine learning. They have different strengths and weaknesses, and they may suit different types of projects and devices. Therefore, it is important to understand their main characteristics and capabilities, and how they differ from each other.

The following table summarizes some of the key aspects of each framework, such as the supported devices, languages, libraries, models, and tools. You can use this table as a quick reference, but you should also read the detailed descriptions of each framework in the following subsections.

Framework	Supported Devices	Supported Languages	Supported Libraries	Supported Models	Supported Tools
TensorFlow Lite	Android, iOS, Linux, Windows, macOS, microcontrollers, and more	Python, C++, Java, Swift, Objective-C, and more	TensorFlow, Keras, NumPy, SciPy, and more	Any TensorFlow model, including pre-trained models and custom models	TensorFlow Lite Converter, TensorFlow Lite Interpreter, TensorFlow Lite for Microcontrollers, TensorFlow Model Optimization Toolkit, TensorFlow Lite Support Library, TensorFlow Lite Benchmark Tool, and more
PyTorch Mobile	Android, iOS, Linux, Windows, macOS, and more	Python, C++, Java, Objective-C, and more	PyTorch, TorchVision, TorchAudio, TorchText, and more	Any PyTorch model, including pre-trained models and custom models	PyTorch Mobile Interpreter, TorchScript, PyTorch Quantization, PyTorch Model Optimization, PyTorch Mobile Profiler, and more
Edge Impulse	Any device that can run C++, such as Arduino, ESP32, STM32, Raspberry Pi, and more	C++, Python, JavaScript, and more	TensorFlow, Keras, OpenCV, Scikit-learn, and more	Any model that can be converted to TensorFlow Lite for Microcontrollers, including pre-trained models and custom models	Edge Impulse Studio, Edge Impulse CLI, Edge Impulse SDK, Edge Impulse Ingestion Service, Edge Impulse EON Compiler, Edge Impulse Deploy Block, and more
MicroPython	Any device that can run Python, such as micro:bit, PyBoard, ESP8266, ESP32, and more	Python and its variants, such as CircuitPython, Adafruit Blinka, and more	NumPy, SciPy, Matplotlib, Pandas, and more	Any model that can be converted to a MicroPython module, such as pre-trained models and custom models	MicroPython REPL, MicroPython Compiler, MicroPython Cross-Compiler, MicroPython Firmware, MicroPython Libraries, and more

As you can see, each framework has its own advantages and disadvantages, and they may not be compatible with each other. Therefore, you need to carefully evaluate your project requirements and choose the framework that best suits your needs and preferences. In the next section, you will learn how to choose the best framework for your project, based on some criteria and questions.

4.1. TensorFlow Lite

TensorFlow Lite is a framework for embedded machine learning that is based on TensorFlow, one of the most popular and widely used frameworks for deep learning. TensorFlow Lite enables you to run TensorFlow models on devices with limited resources, such as microcontrollers, sensors, or edge devices.

TensorFlow Lite provides several features and benefits for embedded machine learning development, such as:

Model conversion: TensorFlow Lite allows you to convert your TensorFlow models into a smaller and more efficient format that can run on embedded devices. You can use the TensorFlow Lite Converter to convert your models into TensorFlow Lite FlatBuffers, which are binary files that contain the model architecture and weights.
Model optimization: TensorFlow Lite also allows you to optimize your models for better performance and lower power consumption on embedded devices. You can use the TensorFlow Lite Optimizer to apply techniques such as quantization, pruning, and clustering to reduce the model size and latency.
Model deployment: TensorFlow Lite also allows you to deploy your models on embedded devices easily and securely. You can use the TensorFlow Lite Interpreter to run your models on various platforms, such as Android, iOS, Linux, and microcontrollers. You can also use the TensorFlow Lite for Microcontrollers library to run your models on devices with as little as 16 KB of RAM.
Model testing: TensorFlow Lite also allows you to test your models on embedded devices and monitor their performance and behavior. You can use the TensorFlow Lite Benchmark Tool to measure the inference speed and accuracy of your models on different devices. You can also use the TensorFlow Lite Model Maker to create and test custom models for specific tasks, such as image classification, text classification, and object detection.
Model monitoring: TensorFlow Lite also allows you to monitor your models on embedded devices and update them remotely. You can use the TensorFlow Lite Task Library to access pre-trained models for common tasks, such as image segmentation, pose estimation, and natural language understanding. You can also use the TensorFlow Lite Support Library to handle common operations, such as data preprocessing, postprocessing, and visualization. You can also use the TensorFlow Lite AAR to integrate your models into Android applications and update them over the air.

As you can see, TensorFlow Lite is a powerful and versatile framework for embedded machine learning that can help you create and deploy amazing applications on devices with limited resources. However, TensorFlow Lite is not the only framework for embedded machine learning. In the next sections, you will learn about other frameworks, such as PyTorch Mobile, Edge Impulse, and MicroPython, and see how they compare to TensorFlow Lite.

4.2. PyTorch Mobile

PyTorch Mobile is a framework for embedded machine learning that is based on PyTorch, another popular and widely used framework for deep learning. PyTorch Mobile enables you to run PyTorch models on devices with limited resources, such as mobile phones, tablets, or edge devices.

PyTorch Mobile provides several features and benefits for embedded machine learning development, such as:

Model conversion: PyTorch Mobile allows you to convert your PyTorch models into a smaller and more efficient format that can run on embedded devices. You can use the torch.jit module to convert your models into TorchScript, which is a subset of Python that can be serialized and executed by a C++ runtime.
Model optimization: PyTorch Mobile also allows you to optimize your models for better performance and lower power consumption on embedded devices. You can use the torch.quantization module to apply techniques such as quantization and quantization-aware training to reduce the model size and latency. You can also use the torch.utils.mobile_optimizer module to apply techniques such as freezing and inlining to optimize the model graph and remove unnecessary operations.
Model deployment: PyTorch Mobile also allows you to deploy your models on embedded devices easily and securely. You can use the torch.utils.bundled_inputs module to bundle your models with sample inputs and metadata, which can help with model validation and debugging. You can also use the PyTorch Android and PyTorch iOS libraries to run your models on various platforms, such as Android, iOS, and MacOS.
Model testing: PyTorch Mobile also allows you to test your models on embedded devices and monitor their performance and behavior. You can use the torch.testing module to compare the outputs of your models on different devices and platforms. You can also use the PyTorch Profiler to measure the inference speed and memory usage of your models on different devices.
Model monitoring: PyTorch Mobile also allows you to monitor your models on embedded devices and update them remotely. You can use the PyTorch Mobile Interpreter to access the model internals and modify them at runtime. You can also use the PyTorch Mobile Code Update feature to update your models over the air without requiring a full app update.

As you can see, PyTorch Mobile is a powerful and versatile framework for embedded machine learning that can help you create and deploy amazing applications on devices with limited resources. However, PyTorch Mobile is not the only framework for embedded machine learning. In the next sections, you will learn about other frameworks, such as Edge Impulse and MicroPython, and see how they compare to PyTorch Mobile.

4.3. Edge Impulse

Edge Impulse is a framework that enables you to create and deploy embedded machine learning applications using a web-based platform. Edge Impulse provides a complete solution for data collection, model training, model conversion, model deployment, and model testing. You can use Edge Impulse to build applications for various devices, such as Arduino, Raspberry Pi, STM32, and more.

Some of the features and benefits of Edge Impulse are:

It supports various types of data, such as images, audio, accelerometer, and sensor data.
It offers a user-friendly interface that guides you through the entire development process.
It allows you to use pre-trained models or create your own models using TensorFlow or PyTorch.
It optimizes your models for your target device using techniques such as quantization, pruning, and compression.
It generates code that you can easily integrate with your device firmware.
It provides tools to test and monitor your models on real devices.

Edge Impulse is a great framework for embedded machine learning if you want to:

Use a web-based platform that simplifies the development process.
Work with different types of data and devices.
Leverage the power of TensorFlow and PyTorch.
Optimize your models for performance and efficiency.
Test and monitor your models on real devices.

How do you get started with Edge Impulse? You can follow these steps:

Create an account on the Edge Impulse website.
Connect your device to the Edge Impulse platform using the Edge Impulse CLI or the Edge Impulse mobile app.
Collect data from your device and label it using the Edge Impulse Studio.
Train your model using the Edge Impulse Studio or the Edge Impulse SDK.
Convert your model to a format that is compatible with your device using the Edge Impulse Studio.
Deploy your model to your device using the Edge Impulse Studio or the Edge Impulse SDK.
Test and monitor your model on your device using the Edge Impulse Studio or the Edge Impulse SDK.

Do you want to learn more about Edge Impulse? You can visit their website, read their documentation, or check out their examples and tutorials.

4.4. MicroPython

MicroPython is a framework that enables you to run Python code on microcontrollers and other low-resource devices. MicroPython is a port of the Python programming language that is optimized for embedded systems. MicroPython provides a subset of the Python standard library and supports many of the Python features, such as variables, data types, control structures, functions, classes, modules, and exceptions.

Some of the features and benefits of MicroPython are:

It allows you to use Python, a popular and easy-to-learn programming language, for embedded development.
It supports various microcontroller boards, such as ESP32, STM32, BBC micro:bit, and more.
It offers a REPL (read-eval-print loop) that lets you interact with your device and run Python commands in real time.
It has a built-in library called machine that provides access to the hardware features of your device, such as GPIO, ADC, PWM, I2C, SPI, and more.
It has a built-in library called network that provides support for networking protocols, such as WiFi, Bluetooth, MQTT, and more.
It has a built-in library called uTensor that allows you to run TensorFlow Lite models on your device.

MicroPython is a great framework for embedded machine learning if you want to:

Use Python for embedded development.
Work with various microcontroller boards.
Interact with your device and run Python commands in real time.
Access the hardware features of your device.
Use networking protocols on your device.
Run TensorFlow Lite models on your device.

How do you get started with MicroPython? You can follow these steps:

Choose a microcontroller board that supports MicroPython and connect it to your computer.
Download and install the latest version of MicroPython firmware for your board.
Use a serial terminal program, such as PuTTY or minicom, to access the REPL on your board.
Write and run Python code on your board using the REPL or a text editor, such as Thonny or Mu.
Use the machine and network libraries to interact with the hardware and networking features of your board.
Use the uTensor library to run TensorFlow Lite models on your board.

Do you want to learn more about MicroPython? You can visit their website, read their documentation, or check out their examples and tutorials.

5. How to Choose the Best Framework for Your Project?

By now, you have learned about some of the most popular frameworks for embedded machine learning, such as TensorFlow Lite, PyTorch Mobile, Edge Impulse, and MicroPython. You have seen how they differ in terms of features, benefits, and limitations. But how do you decide which one is the best for your project?

There is no definitive answer to this question, as different frameworks may suit different needs and preferences. However, there are some general criteria that you can use to evaluate and compare the frameworks, such as:

Compatibility: How well does the framework support your target device and platform? Does it offer cross-platform compatibility and interoperability? Does it support the hardware and software features that you need, such as sensors, cameras, accelerometers, Bluetooth, Wi-Fi, etc.?
Performance: How fast and efficient is the framework in terms of model inference, memory usage, power consumption, and latency? Does it offer any optimization techniques, such as quantization, pruning, or compression, to reduce the model size and improve the speed? Does it support hardware acceleration, such as GPU, DSP, or NPU, to boost the performance?
Usability: How easy and intuitive is the framework to use and learn? Does it offer a user-friendly interface, such as a graphical or web-based tool, to simplify the development process? Does it provide comprehensive documentation, tutorials, examples, and community support to help you get started and troubleshoot?
Functionality: How rich and diverse is the framework in terms of features and functionalities? Does it offer a wide range of pre-trained models, libraries, and APIs to help you with data collection, model training, model conversion, model deployment, model testing, and model monitoring? Does it support custom models, custom layers, and custom operations, to allow you to create your own solutions?
Flexibility: How adaptable and customizable is the framework to your specific needs and requirements? Does it allow you to modify and fine-tune the parameters, settings, and options of the framework, such as the model architecture, the optimization technique, the inference engine, etc.? Does it support multiple languages, frameworks, and formats, to enable you to integrate and switch between different tools and platforms?

Based on these criteria, you can compare and contrast the frameworks that you are interested in, and see which one meets your expectations and goals. You can also try out the frameworks on your own device, and see how they perform in practice. You can use online tools, such as TensorFlow Lite Benchmark Tool, PyTorch Mobile Benchmark App, Edge Impulse Throughput Tester, and MicroPython Benchmarks, to measure and compare the performance of the frameworks on your device.

Choosing the best framework for embedded machine learning is not an easy task, as there are many factors to consider and trade-offs to make. However, by using the criteria above, and by experimenting with the frameworks yourself, you can make an informed and confident decision that will help you create amazing embedded machine learning applications.

6. Conclusion

In this blog, you have learned about embedded machine learning, which is the process of running machine learning models on devices with limited resources, such as microcontrollers, sensors, or edge devices. You have also learned why using a framework for embedded machine learning can help you simplify and streamline the development process, and what are the main features and benefits of different frameworks, such as TensorFlow Lite, PyTorch Mobile, Edge Impulse, and MicroPython. Finally, you have learned how to choose the best framework for your project, based on some general criteria, such as compatibility, performance, usability, functionality, and flexibility.

By choosing the best framework for your project, you can create amazing embedded machine learning applications that can run on various devices, such as smart home devices, wearable health monitors, industrial automation, and autonomous vehicles. You can also leverage the power of edge computing, which enables you to run your models locally, without relying on the cloud or the internet. This can improve the speed, efficiency, privacy, and security of your applications.

Embedded machine learning is a rapidly growing and evolving field, with new frameworks, tools, and techniques emerging every day. Therefore, it is important to keep yourself updated and informed about the latest developments and trends in this field. You can also join online communities, such as TensorFlow Lite Community, PyTorch Mobile Forum, Edge Impulse Forum, and MicroPython Forum, to learn from other developers, share your experiences, and get feedback and support.

We hope that this blog has helped you understand and appreciate the potential and possibilities of embedded machine learning, and inspired you to start your own projects using the best framework for your needs. Thank you for reading, and happy coding!