Embedded Machine Learning: Small Machines, Big Brain Power

Embedded Machine Learning
AI generated
Table of Contents

Due to miniaturization, embedded systems are increasingly essential in our interconnected world. Small-scale computers in everyday objects are changing our routines; smartwatches track health metrics, and self-driving cars navigate cities autonomously. 

Central to this transformation is machine learning (ML), a branch of artificial intelligence (AI) that enables computers to learn from experience rather than explicit programming.

An exciting development in this field is embedded machine learning, which allows ML algorithms to run on various hardware platforms, such as smart home devices, computers, and cell phones, despite constraints like limited memory and processing power.

This article explores ML and embedded systems and their benefits, challenges, and future implications.

What is Embedded Machine Learning?

Embedded machine learning refers to performing machine learning on embedded systems such as smartphones, Internet of Things (IoT) devices, wearables, self-driving cars, and industrial machines.

The major goal is to utilize these devices’ computing capabilities for onsite data processing, decision-making, inference generation, etc., without depending on cloud facilities or remote PCs. This approach enhances device autonomy, efficiency, and capability to handle complex computations internally.

The process involves training machine learning models, such as neural networks, in centralized environments like cloud servers or computing clusters. These models subsequently execute operations exclusively on the embedded devices.

The capacity of embedded machine learning lies in its ability to utilize the processing power of numerous embedded controls situated in various environments, including smart buildings, residential areas, manufacturing facilities, and industrial plants. Additionally, it enables the analysis of data generated by a wide array of IoT devices.

What are the Benefits of Using Machine Learning in Embedded Systems?

Using ML models on embedded devices brings several advantages compared to traditional cloud-based AI. These include saving power, making better use of network bandwidth, ensuring privacy, improving environmental impact, and speeding up response times.

For creating predictive models using data from sources like IoT sensors, AI and ML are essential. Integrating ML into embedded systems is challenging because of power and memory limits, but it helps tap into valuable sensor data often ignored due to cost or bandwidth constraints.

  • Valuable Data: Microcontroller units are found in many industrial and consumer devices, making it challenging to integrate ML models due to power and memory constraints. ML helps unlock the ability of sensor data that is often overlooked due to cost, power, or bandwidth limitations.
  • AI on the device: This tech connects smartphones, wearables, in-car cameras, and other IoT sensors such as programmable logic controllers and edge computing gateways; robots, medicine, manufacturing, and predictive maintenance can thus benefit from on-device AI.
  • Embedded ML Ecosystem: Embedded machine learning applications run on various devices and are supported by specific tools and techniques. This ecosystem involves device vendors and original equipment manufacturers integrating ML models into their products, extending to IoT, known as artificial Intelligence of Things (AIoT), facilitating the development and deployment of ML models on embedded devices.

6 Applications of Machine Learning in Embedded Systems

Machine learning algorithms can learn from data, which means that embedded systems can adapt themselves in response to changing circumstances. Consider, for example, the thermostat, which can review past temperature trends and change settings immediately for the best possible comfort. 

This breakthrough points towards an era of more advanced embedded systems with potential in the following areas:

1. Performance Optimization

ML algorithms analyze sensor data to optimize resource utilization. This includes extending battery life in wearables, improving fuel efficiency in cars, and managing processing power in industrial plants.

2. User Experience Enhancement

Embedded systems can learn from individual user behaviors to personalize responses. For example, smart speakers can recognize different voices and suggest recipes based on dietary preferences.

3. Predictive Maintenance

Machine learning algorithms analyze data from sensors embedded in machines to predict potential failures or maintenance requirements. This proactive approach helps prevent downtime and costly repairs, ensuring continuous operation and efficiency.

4. Real-time Image and Speech Recognition

By leveraging neural networks, embedded systems achieve real-time image and speech recognition capabilities. This capability finds applications in security systems, virtual assistants, and autonomous vehicles, enhancing their functionality and responsiveness.

5. Energy Optimization

Machine learning algorithms optimize power usage by analyzing patterns in energy consumption data. This leads to improved energy efficiency and extended battery life in devices such as wearables, cars, and industrial plants, contributing to the sustainable operation and reduced environmental impact.

6. Anomaly Detection

ML algorithms embedded in systems can recognize exceptions in different domains, including cybersecurity, production processes, and health supervision. This feature exposes abnormal sensor readings and anticipates data security violations, equipment breakdowns, or illness, thus guaranteeing safety and dependability.

Challenges for Implementing Machine Learning within Embedded Systems

Machine learning models are often large and demand a lot of computational power, making them tough to implement in environments with limited resources.

Embedded systems, usually with restricted processing power and memory, find it challenging to handle these resource-heavy models effectively. This limitation becomes especially pronounced when considering tasks like data storage. Gathering and storing large datasets necessary for training ML models can prove impractical for embedded systems with constrained storage capacities.

Another critical factor is real-time operation. Many embedded systems require decisions to be made swiftly within strict timeframes. However, the computational complexity of advanced ML models can hinder their ability to deliver timely results in these scenarios.

Moreover, security remains a significant concern. These systems are often interconnected, exposing them to various security threats. Machine learning models themselves can also be vulnerable to manipulation, exacerbating these security risks.

Additionally, optimizing ML algorithms to function successfully across different types of hardware poses a significant challenge. It necessitates a deep understanding of the unique characteristics and limitations of each embedded device.

Power consumption adds to these challenges, particularly since many embedded devices rely on battery power. Running complex ML models can accelerate battery drain, impacting the device’s operational longevity.

Furthermore, edge computing – where data collection and processing occur locally – raises additional privacy and security considerations. Ensuring secure data handling through robust encryption becomes essential, albeit adding complexity to the implementation of ML solutions in embedded systems.

Adapting Machine Learning for Embedded Systems

There are always new methods that researchers use to tackle these challenges. Here are a few of the most promising techniques:

  • Model Compression and Quantization: Machine learning models can be reduced in size significantly by techniques such as trimming and quantization to make them suitable for embedded systems with limited resources.
  • Federated Learning: This method allows many devices to collaborate in training a machine learning model while not exposing their data. It helps to keep data privacy safe and reduces the storage burden on each device.
  • Edge Computing: Performing computations closer to where the data is generated (on the edge device itself) rather than in the cloud can save time otherwise spent on latency, leading to improved performance in real-time applications.
  • Custom Hardware Design: Creating specialized hardware architectures for the efficient execution of particular machine learning tasks may overcome processing limitations inherent in embedded system environments.

However, it doesn’t stop here – researchers are relentless in their pursuit of breakthroughs that will keep pace with today’s ML demands while using low-powered chips.

Embedded Machine Learning: Key Takeaways

Embedded machine learning is pushing the boundaries of what small machines and low resources can achieve, meeting the demands of modern machine learning applications. This shift reduces reliance on cloud infrastructure, enhancing response times, ensuring data privacy, and optimizing power and network bandwidth utilization.

Despite challenges like limited processing power and security risks, ongoing advancements in model compression, federated learning, edge computing, and custom hardware design are addressing these issues. 

With continual innovation expected from researchers, embedded ML will benefit applications in healthcare, manufacturing, smart cities, and beyond. This evolution promises to drive future advancements in IoT and AI integration, shaping a more connected and intelligent world. 

Subscribe to our newsletter

Keep up-to-date with the latest developments in artificial intelligence and the metaverse with our weekly newsletter. Subscribe now to stay informed on the cutting-edge technologies and trends shaping the future of our digital world.

Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.