Introduction to Embedded AI – Applications | Examples | Working I

 

We all know Artificial Intelligence is being built into the things we use daily, like phones, cars, and even refrigerators. However, this is called embedded AI. It makes these devices smarter by letting them think and act on their own when necessary. As a result, this helps us do things faster, saves time, and opens up many new ways to use technology. 

Embedded AI Meaning

Embedded Artificial Intelligence is putting tiny brains into small devices. These brains use advanced technology called machine learning and deep learning to learn from their surroundings, understand what you like, and do things on their own without needing you to tell them. 

 

As discussed earlier, AI is being built into small, affordable devices that don’t need a big computer (like the cloud) to work. Moreover, these devices are getting more and more popular, with millions being sold every year. To make these devices advanced, we need remarkable skills that go beyond just knowing about computers and AI. We need to understand how devices work, how sensors collect information, and how to process things like videos and sounds quickly. Unique tools and software also needed to create these devices.

 

AI models for small devices should be small because they run on tiny computers. Also, these models are often just a few kilobytes big and do one specific thing, like recognizing faces or understanding voice commands. They trained by collecting data from sensors and using a big computer to teach them. Once they are trained, they’re put onto the small device. The device then uses this model to understand the information it gets from the sensors.

Features of AI in Embedded Systems

Embedded AI devices are designed to operate autonomously, often with limited resources like RAM, flash memory, and battery power. In short, they should process data and make decisions locally, without relying heavily on external networks or cloud infrastructure.

 

Key characteristics of AI embedded systems:

 

• Local Processing: These devices perform AI tasks directly on the device itself, rather than sending data to a remote server.
• Low Power Consumption: They are energy-efficient, often using specialized hardware or software techniques to minimize power usage.
• Limited Resources: They operate with constrained computing power, memory, and storage capabilities.
• Real-time Performance: They are often required to process data and make decisions in real-time, with minimal latency.
• Specific Applications: Embedded AI typically tailors for detailed use cases, such as IoT devices, wearables, and automotive systems.

EAI Working

Embedded AI helps us do things faster, smarter, and more efficiently. It's changing the way we live and work. In addition, it is a tiny computer inside a device that can learn and think for itself. 

 

• Collecting Information: It gathers data from the world around it, like what a camera sees or what a temperature sensor feels.
• Learning and Understanding: This advanced software is designed to deeply understand the data and uncover valuable insights from it.
• Model Storage: This module stores the "models" or algorithms that the AI system uses. These models teach the system how to perform specific tasks. They usually create by training the AI on large datasets.
• Model Protection: The system ensures that the latest and most accurate models are always used, preventing accidental deletion or overwriting. 
• Making Savvy Decisions: With its acquired knowledge, it has the ability to make informed decisions and execute them effectively. 
• Inference: This module applies the models to the preprocessed data to make predictions or decisions. For instance, in a self-driving car, this module might use a model to determine the distance to an obstacle based on sensor data.
• Result Delivery: Once the inference is complete, the module sends the results to the relevant part of the device. In the car example, this might be the steering or braking system. 

 

Embedded systems and AI are a process of collecting data, processing it, applying intelligent algorithms, and using the results to make decisions or take actions. In addition, it's a powerful technology that's transforming a wide range of industries, from healthcare to transportation.

Embedded AI Applications

AI ECN can automatically adjust its network traffic. Moreover, it is a smart system that helps networks run smoothly. In addition, it can modify settings to keep data moving quickly and without errors. As a result, this is especially important for services that can't afford to lose any information. Let's explore some of the top 10 AI for embedded systems applications in more detail:

 

• Autonomous Vehicles: These cars use cameras, sensors, and AI algorithms to understand their surroundings, make decisions, and drive safely. In addition, it reduces accidents, improves traffic flow, and offers a new level of convenience.
• Smart Home Devices: Devices like thermostats, lights, and security cameras use AI to learn your preferences and automate tasks. Moreover, it increases energy efficiency, improves security, and enhances convenience.
• Healthcare Devices: Wearable devices and medical imaging equipment use AI to monitor health, detect anomalies, and assist in diagnosis. Also, it enables early detection of diseases, improves patient care, and reduces healthcare costs.
• Industrial Automation: Robots and machines equipped with AI can perform tasks with greater precision, efficiency, and adaptability. Apart from that, it increases productivity, improves quality control, and reduces downtime.
• Agricultural Technology: Drones, sensors, and AI algorithms help farmers optimize crop management, monitor soil conditions, and predict weather patterns. Moreover, it improves crop yields, reduces waste, and promotes sustainable agriculture.
• Retail and Supply Chain Management: AI-powered systems can optimize inventory management, improve customer service, and predict demand. Plus, it reduces costs, improves efficiency, and enhances customer satisfaction.
• Energy Management: AI-powered systems can optimize energy consumption, balance supply and demand, and integrate renewable energy sources. It reduces energy costs, improves grid stability, and promotes sustainability.
• Consumer Electronics: AI enhances user experience by providing personalized recommendations, voice control, and improved performance. Additionally, it increases user satisfaction and improves product functionality.

 

After understanding EAI applications, we will move further towards the examples.

Embedded AI Examples

Examples of Embedded AI include:
 

• Predicting problems: AI can help us see concerns before they happen, like when a machine might break down.
• Smart homes: AI makes our homes more comfortable and energy-efficient.
• Seeing and understanding: Artificial Intelligence helps cameras see things like people and objects, even in low light.
• Self-driving cars: AI helps cars drive themselves safely.
• Better health: AI can help doctors find diseases early and give patients the right treatment.
• Farming made easier: AI helps farmers grow crops and protect them from problems.
• Shopping made simpler: AI helps stores keep track of their products and avoid shortages.
• Traffic management: AI helps keep our roads moving smoothly.

 

AI is making our lives easier, safer, and more efficient.

Wrapping Up Thoughts! 

As we’ve seen embedded AI is revolutionizing the way we interact with technology. By integrating artificial intelligence into a wide range of devices, we're unlocking new possibilities and improving efficiency across various industries. From autonomous vehicles and smart homes to healthcare and industrial automation, it is transforming the world around us. As technology continues to advance, we can expect to see even more innovative and impactful applications of embedded AI in the future. 

 

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