Machine Learning in Embedded Systems: A New Era of Intelligent Devices

Machine learning (ML), a subfield of artificial intelligence (AI), is revolutionizing various industries by enabling systems to learn from data and make intelligent decisions without explicit programming. This capability is particularly transformative when applied to embedded systems, creating a new paradigm known as embedded machine learning or TinyML. This article explores the intersection of machine learning and embedded systems, highlighting the benefits, challenges, applications, and future trends of this rapidly evolving field.

Introduction to Machine Learning

Machine learning involves developing statistical algorithms that can learn from data and generalize to unseen information, performing tasks without explicit instructions. It's a technique used to build programs by giving computers the ability to learn without requiring explicit programming. It works by processing raw data and transforming it into useful information at the application level. Unlike traditional computer programs where developers explicitly code the logic, machine learning algorithms extract rules from data during the training process.

Traditional Software vs. Machine Learning

Traditional software consists of an engineer who explicitly codes an algorithm that takes input, applies the same logic every time, and returns an output. The same output will always come from an identical input. If traditional software were to be used to predict when an industrial machine would break down, an engineer would need to know which metrics in the data indicate a problem and then write code that specifically looks for them to forecast breakdowns. This method works well for several problems. For instance, developing a program to determine whether water is boiling based on its present temperature and altitude is easy because we know water boils at 100°C at sea level. However, determining the precise combination of variables that foretells a particular state can be extremely challenging in many cases.

In contrast, in machine learning, a sizable amount of data must be collected. This data is then fed to a machine learning algorithm, which learns patterns and invents, creates, and deduces its own specific set of rules about the data to make generalizations on new data. In other words, machine learning practitioners are not required to understand which metrics in the data to pay attention to. The machine learning algorithm builds a model of the system based on the data it’s supplied and then uses this model to make predictions in a process called inference. Machine learning serves tasks involving pattern recognition well, especially when dealing with complicated patterns that could be challenging for a human observer to recognize.

Types of Machine Learning

Machine learning approaches can be divided into three main categories:

Supervised Learning: Labelled data can be learned.
Unsupervised Learning: Hidden patterns in unlabelled data can be found.
Reinforcement Learning: A system can learn from its immediate environment by a trial-and-error approach.

The learning process is known as the model’s “training phase,” and it is frequently carried out utilizing computer architectures with plenty of processing power, like several GPUs. The trained model is then applied to new data to make intelligent decisions after learning. The inference phase of the implementation is what is referred to as this procedure.

Embedded Systems: The Foundation for Intelligent Devices

Embedded systems are specialized computer systems designed to perform one or a few dedicated functions, often with real-time computing constraints. These systems are ‘embedded’ as a part of a complete device system, including hardware and mechanical parts. Examples of embedded systems range from small devices like digital watches and MP3 players to large installations like traffic lights, factory controllers, or the systems controlling nuclear power plants.

The Convergence: Machine Learning in Embedded Systems

The intersection of machine learning and embedded systems offers a novel paradigm, often referred to as ‘edge computing’ or ‘edge AI’. The premise of this paradigm is that by incorporating machine learning capabilities directly into embedded systems - on the ‘edge’ of the network, rather than in a centralized cloud - numerous benefits can be realized, including lower latency, improved privacy, and reduced data transmission costs.

The Rise of TinyML

While performing machine learning tasks on smaller board computers is not new, being able to run them on microcontrollers, which are less powerful and complex than small board computers and contain less advanced processors, opens up a world of opportunities that is likely to foster a new generation of AI-powered electronics. The type of embedded machine learning that uses extremely small pieces of hardware, such as ultra-low-power microcontrollers, to run ML models is called TinyML.

Benefits of Embedded Machine Learning

Using machine learning on embedded devices has several significant benefits, succinctly captured by Jeff Bier’s acronym, BLERP:

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Bandwidth: Little to no internet connectivity is required for inference.
Latency: On-device machine learning results in reduced latency. This is because the data does not need to be transferred to a server for inference since the model operates on the edge device.
Energy Efficiency: Since they require so little power, microcontrollers can run continuously for extended periods of time before needing to be recharged.
Reliability: Embedded machine learning is more reliable overall.
Privacy: User privacy is preserved, and misuse is less likely when data is processed on an embedded system rather than in the cloud. Due to the edge computing nature of the architecture, your data is not stored on servers.

Embedded machine learning eliminates the need for data transfer and storage of data on cloud servers. This lessens the likelihood of data breaches and privacy leaks, which is crucial for applications that handle sensitive data such as personal information about individuals, medical data, information about intellectual property (IP), and classified information.

Why Embedded Machine Learning?

Since internet connectivity is a prerequisite for cloud applications, not all machine learning can be done in the cloud. Just imagine you’re in a self-driving car on the highway, and it loses connection to the internet. How scary would that be? Sometimes, machine learning applications must perform processing locally to get the job done. This means putting plenty of computing power into small devices. In recent years, researchers have made significant progress in running machine learning algorithms on tiny microcontrollers. This has led us into the era of embedded machine learning, or TinyML, as it’s often referred to.

Challenges and Opportunities in Embedded Machine Learning

Integrating machine learning with embedded systems is a complex task, fraught with challenges. These include resource constraints of embedded devices (like limited processing power, memory, and energy), the need for real-time or near-real-time response, and the complexity of deploying and updating machine learning models on embedded devices.

Resource Constraints

Embedded devices often require very low power consumption to extend battery life, which is especially important for edge devices. Because these devices usually have limited compute capacity (for example low-frequency processors), storage, and memory, models must be highly optimized. Limited compute resources make running complex or large models difficult.

Model Optimization

Models typically require pruning, quantization, and other optimizations to run efficiently in constrained environments, which increases development complexity and time. Models typically require pruning, quantization, and other optimizations to run efficiently in constrained environments, which increases development complexity and time.

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Deployment and Updates

Remote updates or model replacement can be challenging for field-deployed devices, requiring robust firmware update mechanisms.

Overcoming the Challenges

However, these challenges are not insurmountable. Recent advancements in machine learning algorithms, hardware, and software tools have made it increasingly feasible to deploy ML models on embedded systems. Algorithms such as decision trees, random forests, and lightweight neural networks are particularly suitable for embedded systems due to their efficiency. Hardware accelerators, like application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs), can provide the necessary computational power. Software tools, like TensorFlow Lite and TinyML, can facilitate the deployment and updating of ML models on embedded devices.

Optimization Techniques

At both the algorithm and hardware levels, optimization techniques for classical machine learning and deep learning algorithms are being investigated such as pruning, quantization, reduced precision, hardware acceleration, etc. to enable the efficient execution of machine learning models in mobile devices and other embedded systems.

Machine Learning Frameworks and Development Environments

There are many machine learning frameworks and libraries, each with distinct characteristics, strengths, and weaknesses.

TensorFlow

TensorFlow is an open-source machine learning framework developed by Google, primarily for building and training deep learning models. It supports multiple programming languages, most commonly Python, and also offers C++, Java, and Go interfaces. Earlier TensorFlow versions used static computation graphs, allowing the entire graph to be defined before execution, which aids optimization and deployment. Since TensorFlow 2.0, eager execution is supported, making code more intuitive and easier to debug. TensorFlow supports multiple hardware backends such as CPUs, GPUs, and TPUs, and spans environments from mobile devices to large servers. It has a rich ecosystem of tools and libraries, including TensorBoard for visualization, TensorFlow Lite for mobile and embedded deployment, and TensorFlow Serving for production deployment. TensorFlow benefits from a mature ecosystem, extensive documentation, and large community support. It is well suited for large-scale, enterprise-grade applications and offers comprehensive solutions from development to deployment. In 2017, Google introduced a new version of TensorFlow called TensorFlow Lite. TensorFlow Lite is optimized for use on embedded and mobile devices. TensorFlow Lite eliminates the need to program the matrix multiplications manually and enables high-level neural network operation on microcontrollers.

PyTorch

PyTorch is an open-source deep learning framework developed by Meta AI Research, known for its dynamic computation graph capability and strong adoption in the research community. PyTorch constructs computation graphs dynamically at runtime, which simplifies debugging and model modification. Its syntax is concise and Pythonic, making it well suited for rapid prototyping. PyTorch provides robust GPU acceleration and supports conversion to deployable formats via TorchScript. PyTorch is praised for flexibility and ease of learning, especially for developers with Python experience. Production deployment support historically lagged behind TensorFlow, but improvements like TorchScript have narrowed the gap.

Keras

Keras is a high-level neural network API originally created by Fran?ois Chollet to facilitate rapid model construction and experimentation.

Development Environments

A machine learning development environment encompasses the software and hardware used to design, build, train, debug, and deploy models. These environments can be local desktop setups or cloud-based platforms.

Jupyter Notebook: Jupyter Notebook is an open-source interactive notebook environment that allows code execution, data visualization, and inline documentation in a browser. Jupyter supports interactive development where code and results coexist in a single document, making rapid iteration and experimentation convenient. While Python is the dominant language, Jupyter also supports R, Julia, and others. Extensions add features such as enhanced visualization and version control. Jupyter is user-friendly for beginners and professionals, offering strong visualization capabilities via libraries like Matplotlib and Seaborn.
Google Colab: Google Colab is a cloud-based environment built on Jupyter Notebook, provided by Google. Colab runs on Google servers, so no local software installation is required. The platform provides free GPU/TPU acceleration suitable for training many deep learning models and integrates with Google Drive for storage and sharing. Colab requires minimal setup and is suitable for small-scale deep learning experiments and collaborative work.

Choosing the Right Software

Choosing the right software depends on project requirements, developer familiarity, deployment environment, and resource constraints. For deep learning, TensorFlow and PyTorch are primary choices with distinct advantages. For traditional machine learning tasks, scikit-learn is commonly used.

Application Areas of Embedded Machine Learning

Embedded machine learning has gained popularity across various industries thanks to the emergence of ecosystems (e.g., novel advancements in computer architecture and the breakthroughs in machine learning) supporting it with hardware and software. This has facilitated the integration of machine learning models into low-power systems, such as microcontrollers, hence creating a multitude of novel prospects. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more.

Intelligent Sensor Systems

The effective application of machine learning techniques within embedded sensor network systems is generating considerable interest. Numerous machine learning algorithms, including GMMs (Gaussian mixture model), SVMs, and DNNs, are finding practical uses in important fields such as mobile ad hoc networks, intelligent wearable systems, and intelligent sensor networks.

Heterogeneous Computing Systems

Computer systems containing multiple types of processing cores are referred to as heterogeneous computing systems. Most heterogeneous computing systems are employed as acceleration units to shift computationally demanding tasks away from the CPU and speed up the system. Heterogeneous Multicore Architecture is an area of application where to speed up computationally expensive machine learning techniques, the middleware platform integrates a GPU accelerator into an already-existing CPU-based architecture thereby enhancing the processing efficiency of ML data model sets.

Embedded FPGAs

Due to their low cost, great performance, energy economy, and flexibility, FPGAs are becoming increasingly popular in the computing industry. They are frequently used to pre-implement ASIC architectures and design acceleration units. CNN Optimization using FPGAs and OpenCL-based FPGA Hardware Acceleration are the areas of application where FPGA architectures are used to speed up the execution of machine learning models.

Case Studies

Several real-world applications highlight the potential of integrating machine learning with embedded systems.

Automotive Industry: ADAS uses embedded systems equipped with machine learning algorithms to recognize road signs, detect obstacles, and assist in navigation, thereby enhancing safety and convenience.
Healthcare Sector: Wearable devices use embedded machine learning to monitor vital signs, detect anomalies, and even predict medical events, thereby enabling personalized and proactive healthcare.
Google’s Coral Platform: Coral offers a suite of hardware components and software tools designed to facilitate the development and deployment of ML models on embedded systems. The hardware components include a system-on-module (SoM) and a USB accelerator, both equipped with Google’s Edge Tensil, a custom ASIC designed for edge ML. The software tools include a version of TensorFlow Lite that supports the Edge TPU and a model compiler that transforms TensorFlow models into a format optimized for this TPU.
Amazon’s AWS DeepLens: AWS DeepLens, a programmable video camera that integrates an Intel Atom processor and a deep learning software library, allowing developers to run deep learning models on the device for tasks such as object detection, face recognition, and activity recognition.

The Future of Machine Learning and Embedded Systems

The future of machine learning and embedded systems is bright and full of potential. As machine learning algorithms continue to improve, and as hardware and software tools for embedded systems continue to advance, we can expect to see an increasing number of embedded systems with integrated machine learning capabilities.

Promising Directions

Algorithms: Development of more efficient and robust learning algorithms, such as those based on online learning, federated learning, and lifelong learning.
Hardware: Development of more powerful and energy-efficient processors for embedded systems, achieved through techniques like hardware specialization, near-memory computing, and 3D stacking.
Software: Development of more sophisticated tools for deploying and managing machine learning models on embedded devices, with features like automatic model compression, quantization, and pruning, as well as over-the-air model updating.
Applications: Proliferation of intelligent devices in various domains, from smart homes and smart cities to healthcare, transportation, and industrial automation.

Market Growth

Between 2021 to 2026, the global market for embedded AI is anticipated to expand at a 5.4 percent CAGR and reach about USD 38.87 billion, as per the maximize market research group reports.

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