Understanding Embedded AI (EAI): What it is and How it Works
Understanding Embedded AI (EAI): What it is and How it Works
Discover how Embedded AI works and its real-world applications. Dive into the future of AI integration in this blog.
Technology
November 5, 2023
Sadman Sakib Khan -Vetted by Shipankar Sarker (AI ML Expert)
Sadman is a marketing professional and a fervent devotee of the ever-evolving world of marketing & technology. Possessing a knack for crafting compelling narratives, Sadman passionately engages in the creation of top-tier content in close partnership with esteemed subject matter experts. During his leisure hours, he indulges in the art of musical expression, immerses himself in cultural exhibitions, watch anime, and travel.
In the world of rapidly evolving technology, artificial intelligence is the climax of this age. Although the use of artificial intelligence has been there for quite a long time, the emergence of generative AI broke the mold. In this blog, we’ll get an in-depth understanding of what is Embedded AI.
What is Embedded AI (EAI)?
Embedded AI, also known as Embedded artificial intelligence, is like giving brains to gadgets that aren’t very powerful.
According to Huawei, Embedded AI (EAI) is a general-purpose framework system for AI functions. It is built into network devices and provides common model management, data obtaining, and data preprocessing functions for AI algorithm-based functions for these devices. Besides, it supports the function to send inference results to AI algorithm-based functions.
Embedded AI makes devices smarter by using their data and computer skills. This helps save money on sending data, keeps data safe, and lets them make quick decisions.
The Fundamentals of Embedded AI
Embedded AI is like a secret magic trick that makes our everyday gadgets super smart. It’s like a wizard working in the background, making things like recognizing faces, listening to our commands, and suggesting things just for us. Let’s take a closer look to see how it all works.
Concept of Embedded AI
It is the application of machine learning and deep learning algorithms to devices that have limited computing power. The systems are usually deployed on edge devices.
An easier way to understand embedded AI is to think of the differences between edge computing and cloud computing. Processing of the data at the source, where it’s generated is edge computing. But, cloud computing is the processing of data in a centralized data center.
That is why embedded AI systems are usually deployed on edge devices. This means that these devices can process data in real-time. Eliminating the need to send it to the cloud. It is important where latency is critical. Such as self-driving cars and industrial automation systems.
Enabling improved security and privacy. Because the data is processed on the device, protected from unauthorized access.
Key components and technologies involved
Embedded AI systems are composed of the following key components:
Hardware The hardware components include a processor, memory, and storage devices. The processor handles executing the AI algorithms. The memory stores the AI models and data. The storage devices are there to store AI models and data permanently.
Software The software components consist of the operating system, AI framework, and AI model. The operating system manages the hardware resources. It also provides a platform for the AI framework to run on. The AI framework is a framework consisting of different software libraries that provide the tools and functionalities needed to develop and deploy AI models. The AI model is the mathematical representation of the AI algorithm.
The following technologies are involved in embedded AI:
Machine learning is like a smart tool in computer programs that gets better at guessing what will happen. It looks at old information to make better guesses about new things.
Deep learning is a special kind of machine learning that’s inspired by how our brains work. It’s great at understanding tricky things from data. This makes it perfect for many different jobs in AI.
Edge computing is like having mini-computers close to where data comes from. It’s great for embedded AI because it can process data super fast and without delays.
Here are some examples of embedded AI technologies:
TensorFlow Lite: It’s a lightweight machine-learning framework for mobile and embedded devices. It’s optimized for performance and efficiency making it ideal for embedded AI applications.
ONNX Runtime: It’s an open-source inference engine for running machine learning models on a variety of platforms. This includes embedded devices. It’s optimized for performance and efficiency, making it ideal for embedded AI applications.
Coral Edge TPUs: They are a family of machine learning accelerators designed for edge devices. Coral Edge TPUs provide significant performance and efficiency improvements for embedded AI applications.
Historical Development and Evolution of Embedded AI
The historical development and evolution of embedded AI can be traced back to the 1950s. Which was the early days of AI research. But, it was not until the late 1970s and early 1980s that embedded AI systems began to be deployed in real-world applications.
One of the earliest examples of embedded AI is the D-17B computer. Used in the Minuteman missile guidance system in the 1960s.
The D-17B was a large and complex system. However, it was able to perform basic AI tasks such as pattern recognition and decision-making.
In the 1990s, the development of embedded AI was further developed by the advances in microprocessor technology. Microprocessors have become smaller, faster, and more energy-efficient. Making them ideal for use in embedded systems.
In the early 2000s, the development of machine learning and deep learning algorithms led to a new generation of embedded AI systems. These systems were able to perform more complex AI tasks. Such as image recognition, natural language processing, and speech recognition.
Applications of Embedded AI
Embedded AI is a rapidly growing field with applications in a wide range of industries. Today, embedded AI systems are used in a wide range of applications, including:
- Smartphones and wearables: Embedded AI is applied in smartphones and wearables. This streamlines tasks like image recognition, natural language processing, and voice assistants. It’s used in smartphone cameras to identify faces and objects and in smartwatches for fitness and sleep tracking.
- Smart homes and smart cities: It’s deployed in smart homes and cities to manage devices, track energy use, and streamline traffic.
- Industrial automation systems: Embedded AI enhances industrial automation. It monitors equipment, predicts failures, and optimizes production. For example, it’s in factory robots for defect inspection and in power plants for predicting equipment failures.
- Autonomous vehicles: Embedded AI is crucial for autonomous vehicles. It helps them sense their environment, decide, and navigate.
- Medical devices: Embedded AI advances medical devices, enhancing accuracy in diagnosis with medical imaging and improving surgical precision with robots.
- Robotics: Embedded AI empowers robots to learn, adapt, and tackle complex tasks. This includes exploring hazardous environments and conducting disaster relief operations.
Embedded AI enhances user experiences in a number of ways:
- Personalization: Embedded AI personalizes user experiences by customizing content and recommendations. Like suggesting apps, music, and articles on smartphones based on past behavior.
- Context awareness: Embedded AI enhances device awareness. Such as smart thermostats adapting to user preferences for temperature control.
- Proactive assistance: Embedded AI offers proactive help by anticipating user needs. As seen in voice assistants suggesting tasks based on current activities.
- Natural interaction: Embedded AI fosters natural interaction, as in wearables recognizing gestures and responding intuitively.
How Embedded AI Work?
Embedded AI demands expertise beyond traditional embedded systems. Encompassing knowledge of devices, sensors, and advanced signal-processing methods. It requires specialized software tools and frameworks for development.
Embedded AI systems consist of three modules.
- The data module
- The algorithm module
- The inference module.
The data module handles and processes data from sensors and other sources. The algorithm module integrates many AI algorithms and manages many model files. The inference module processes real-time data with the AI algorithm to implement AI functions based on the results.
Embedded AI is being used in a wide variety of embedded systems. This includes autonomous vehicles, natural language processing, predictive maintenance, and factory automation.
AI and ML algorithms are designed to handle complex scenarios and make accurate decisions in real time. By incorporating this into embedded systems, businesses can streamline processes and automate tasks. Which would have been impossible a decade ago.
TinyML: Embedded AI for Small Devices
TinyML is a subset of machine learning that is specifically designed for use on small, low-power devices. It is a rapidly growing field, with applications in a wide range of industries, including healthcare, consumer electronics, and industrial automation.
TinyML models are typically much smaller and more efficient than traditional machine learning models. This makes them ideal for use on devices with limited resources, such as smartphones, smart speakers, and wearables.
TinyML models can be trained on a variety of data, including sensor data, images, and text. Once trained, the models can be deployed to devices and used to make predictions or decisions without the need for a cloud connection.
TinyML has several advantages over traditional cloud-based AI, including:
Reduced power consumption: TinyML models are typically much more efficient than cloud-based AI models, which can help to extend the battery life of devices.
Improved privacy: TinyML models can be trained on device data without the need to send data to the cloud, which can help to protect the privacy of users.
Faster response times: TinyML models can make predictions or decisions on devices without the need to send data to the cloud, which can result in faster response times for users.
Overall, TinyML is a promising technology with the potential to make embedded AI more accessible to a wider range of applications.
Here are some examples of TinyML in embedded AI:
- On device wake word detection on Siri and Alexa: Siri and Alexa are often preceived as examples of embedded AI, they actually represent hybrid approach involving both on device and cloud processing. However, once activated these virtual assistants rely heavily on cloud-based AI for complex tasks such as speech recognition, natural language understanding and generating apporpriate resposes. This combination of edge and cloud computing allows for a seamless user experience while leveraging the strengths of both processing environments.
- Medical diagnostics: TinyML models can be used to diagnose diseases and conditions based on patient data, such as medical images or wearable sensor data.
- Industrial automation: TinyML models can be used to automate tasks in factories and other industrial settings, such as quality control or predictive maintenance.
TinyML is a rapidly growing field with the potential to revolutionize the way we interact with devices and the world around us.
Benefits and Challenges Embedded AI
embedded AI offers several benefits and challenges, as outlined below:
The Benefits of Embedded AI
- Enhanced automation: AI algorithms can learn to automate routine tasks and processes. Freeing up human operators to focus on more complex or strategic tasks
- Improved efficiency: AI algorithms can optimize processes and reduce waste, leading to more efficient resource utilization and cost savings
- Predictive maintenance: Embedded AI can perform analysis on stored data to identify scenarios. Wherein a failure might occur before the actual failure happens.
- Real-time decision-making: AI and ML algorithms are designed to handle complex scenarios and make accurate decisions in real-time.
- Higher scalability: Edge AI helps applications that rely on real-time data processing by assisting with data, learning models, and inference
- Data protection and security: Edge computing moves data processing and storage closer to the device, reducing reliance on a distant central location. This minimizes latency and redundancy problems that can hinder application efficiency.
- Low operational risks: Embedded AI can autonomously respond to changes, reducing the risk of human error
- Cost-effectiveness: Embedded AI enables small-scale devices to learn from their environment, adapt to user preferences, and autonomously respond to changes. Leading to cost savings in the long run.
- Advanced, near real-time signal processing methods: Embedded AI requires knowledge of devices, sensors, and advanced, near real-time signal processing methods for video, audio, motion, or other signals
The Challenges of Embedded AI
- Model complexity: Embedded devices often have limited resources. So it can be difficult to train complex models on these devices.
- Limited memory and computing resources: AI learning models should have a minimal footprint. So they can be run on microcontrollers that have limited memory and computing resources.
- Specialized knowledge and skills: Embedded AI demands expertise beyond traditional embedded systems, data science, and machine learning.
Case Studies: Embedded AI
Below are several case studies and success stories of embedded AI
Alexa and Siri: Embedded AI allows Alexa and Siri to process user data and respond to commands without the need for a cloud connection. This makes Alexa and Siri faster, more responsive, and more secure.
Pinterest: Pinterest uses fancy tech from NVIDIA to make its search and suggestions better. They used NVIDIA’s TensorRT software to make their suggestions happen faster, like 40% faster!
InData Labs: InData Labs worked with a retail company to make their inventory system better using AI. They looked at sales info and guessed what they’d need in the future. This helped the company save 20% on inventory costs and sell 15% more stuff.
Advantech: Advantech helped a transportation company make traffic better with AI. They looked at traffic data in real time and made things less crowded and safer on the roads.
Edge AI and Vision Alliance: The Edge AI and Vision Alliance has stories about how AI helps in different jobs, like in healthcare, stores, and making stuff. For example, they talk about a super helpful vision system that fixed problems for a fancy audio equipment company.
Amazon Web Services (AWS): AWS and Accenture Velocity teamed up to make AI and machine learning projects faster with CodeWhisperer. This made it possible for Accenture Velocity to train machine-learning models in way less time, up to 90% faster!
Tesla: Tesla puts smart AI in its self-driving cars to see, think, and drive. They use special sensors like cameras, radar, and ultrasound to look all around the car. Then, Tesla’s AI system figures out what things are and how to drive safely, like steering, braking, and going faster when needed.
The Impact of Embedded AI: The Hidden Engine of the Future
Embedded AI is already making a big difference in our lives, but it’s just getting started. These hidden AI systems in our devices are driving some of the coolest new tech stuff.
Think about this: AI is super important for making cars that drive themselves, smart cities that work better, and making stuff in factories smarter. AI also helps create new medical gadgets, make green energy work better, and give people cool personal stuff.
What’s really cool about embedded AI is that it can learn and get better as it goes along. It becomes smarter by gathering more information and experience. That’s why it’s great for solving tricky problems that keep changing.
Here are a few examples of how embedded AI is being used to create a more sustainable, equitable, and prosperous future:
- Embedded AI is helping create new farming tech to grow more food with less stuff. Like robots that can weed and pick crops better.
- Embedded AI is making better medical tools to find and treat diseases. Like machines that can spot cancer earlier and deliver medicines right where they’re needed in the body.
- Embedded AI is creating smarter ways to learn and do better in school. It’s making tutoring that fits what each student needs and tests that tell you how you’re doing more accurately and quickly.
These are just a few examples of the many ways that embedded AI is being used to create a better future for all. As embedded AI technology continues to evolve, we can expect to see even more innovative and impactful applications in the future.
It is powering some of the most transformative technologies of our time. And it has the potential to create a more sustainable, equitable, and prosperous future for all.
The Future of Embedded AI
The future of embedded technology, especially embedded AI, is looking exciting. These cool ideas and improvements are going to make embedded AI even more popular and let us use it in many different jobs. Here are some things we think will happen in the future of embedded AI:
- In the future, embedded systems will use less energy and be kinder to the environment, making our gadgets more eco-friendly.
- Embedded systems will make it easier for machines to talk with people and fit better into our lives. We’ll see more advanced robots doing things on their own and making life more convenient.
- Real-time data processing: Embedded systems will be able to capture, analyze, and react to growing volumes of data in real-time
- Evolving technology will be more accessible. Enabling more people to take advantage of the progress
- Multicore-embedded systems are making it easier and cheaper for companies to join the embedded system market. This gives them more chances to use this cool technology and make things better.
- Artificial intelligence (AI) and machine learning (ML), along with stuff like the Internet of Things (IoT) and computer vision, are making embedded systems even better and more useful.
- Embedded intelligence using real-time data is becoming more important, especially in areas like saving energy and managing stuff like buildings. There are lots of new ways we’re discovering to use it.
- Embedded machine learning and federated machine learning are super smart technologies. They help devices learn better without always needing the internet to send data to the cloud. These cool ideas are changing how we use AI in different jobs.