Edge AI Explained: Uses, How it Works & More
5/17/2024
Edge AI brings artificial intelligence directly to devices at the edge of the network, enabling real-time decision-making, reduced latency, and enhanced privacy. This article explores Edge AI's growing impact across industries, comparing it with other AI approaches, showcasing its benefits, and highlighting its transformative applications.
Key Takeaways
- What is Edge AI? It refers to deploying AI on local devices, enabling faster and more private data processing without reliance on cloud servers.
- Why the Shift to Edge AI? Faster decision-making, reduced network congestion, and offline functionality are driving its adoption.
- Edge AI vs. Other AIs: Edge AI excels in low-latency, real-time applications; Cloud AI offers scalability and computational power.
- Applications: Found in healthcare, smart cities, manufacturing, and more, it enables predictive maintenance, traffic optimization, and personalized experiences.
- Benefits: Enhanced privacy, reduced latency, power efficiency, and scalability.
What is Edge AI?
Edge AI refers to deploying artificial intelligence (AI) algorithms and models directly on edge devices, such as sensors, smartphones, or Internet of Things (IoT) devices, rather than relying on centralized cloud servers for processing. This enables real-time data analysis and decision-making at the edge of the network, without needing to transmit data to a remote server for processing. By running algorithms locally on edge devices, edge computing with artificial intelligence reduces latency, minimizes bandwidth usage, and enhances privacy and security, making it ideal for applications that require fast response times and efficient use of resources.
Why are We Moving to Edge AI?
The shift toward Edge AI is largely driven by the need for faster decision-making, enhanced automation, and increased efficiency across various industries. By bringing AI to the edge, data processing becomes faster, more secure, and less reliant on cloud infrastructure, which reduces network congestion and allows devices to function offline when internet connectivity is limited.
The market for Edge AI is growing rapidly due to advancements in neural networks, computing power, and the proliferation of IoT devices. In 2023, the Edge AI market was valued at approximately USD 20.45 billion, and it is projected to reach USD 269.82 billion by 2032, growing at a compound annual growth rate (CAGR) of 33.3% during this period. This growth reflects the increasing adoption of Edge AI across sectors such as healthcare, automotive, manufacturing, and consumer electronics.
Edge AI is empowering industries by enabling real-time analytics, improving operational efficiency, and enhancing safety. Its application in autonomous driving, smart cities, and industrial automation showcases its potential to revolutionize multiple fields. The move towards Edge AI not only improves performance but also addresses concerns around data privacy and security, making it a key component in the future of AI-driven technologies.
The move towards Edge AI is fueled by the growing need for quick decision-making, automation, and efficiency across different sectors. Bringing AI to the edge allows for faster data processing, better data security, reduced network congestion, and the ability to work offline when internet connectivity is unavailable.
This shift is prompted by the increasing demand for automation, improvements in neural networks, advancements in computing, and the widespread use of IoT devices. Ultimately, AI and edge computing empower devices to make smarter decisions, leading to improved efficiency, safety, and overall effectiveness.
These AI solutions are already driving success globally and continue to innovate: How AI Is Transforming the Industrial Field Nowadays
How is Edge AI Different from Other AIs?
Let's delve into the differences between Edge AI, Distributed AI, and Cloud AI, each offering unique ways to deploy artificial intelligence. We'll uncover their advantages, applications, and key considerations to help you better understand their distinct approaches.
Edge AI vs. Distributed AI
Edge AI enables devices to make decisions locally, reducing the need to constantly transfer data to a central location. However, for training and deploying AI models, data still needs to be sent to the cloud. Deploying edge AI across various locations faces challenges like managing large amounts of data and limited resources.
Distributed AI (DAI) addresses these challenges by collecting data intelligently and automating AI processes. DAI distributes tasks, coordinates performance, and scales applications across multiple systems and devices autonomously.
Unlike edge AI, DAI offers better scalability and flexibility, making it suitable for managing complex AI tasks across diverse environments.
Edge AI vs. Cloud AI
Edge AI and Cloud AI represent two different paradigms for artificial intelligence.
Edge AI involves processing data locally on edge devices, close to the data source. This method offers advantages like low latency, enhanced data privacy, and reduced dependence on network connectivity.
In contrast, Cloud AI involves centralizing the processing of data on remote cloud servers. While Cloud AI offers scalability, flexibility, and easy access to computational resources, it typically introduces higher latency and greater bandwidth usage due to data transmission over the internet. Additionally, Cloud AI may raise concerns regarding data privacy and security, as sensitive data must be transmitted and stored on external servers.
Edge AI |
Cloud AI |
|
Processing Location |
Local device |
Remote cloud servers |
Data Volume |
Limited |
Large |
Latency |
Low (real-time) |
High |
Power Consumption |
Low |
High |
Cost (Hardware/Software) |
Lower |
Higher |
Security |
Potentially lower |
Higher (centralized) |
Offline Functionality |
Yes |
No |
Ideal Applications |
Wearables, sensors, robotics, IIoT devices |
Facial recognition, big data processing |
In conclusion, Edge AI excels in real-time applications on local devices like wearables, IIoT devices, and security cameras, providing low latency and offline functionality. On the other hand, Cloud AI boasts powerful processing capabilities and vast data storage, making it ideal for complex tasks like facial recognition and big data analytics. By understanding the strengths and applications of each approach, it is possible to leverage the most suitable AI technology to meet specific needs and goals.
Benefits of Edge AI
Edge AI revolutionizes data processing on devices, offering numerous advantages:
Scalability
Edge AI offers great scalability, allowing organizations to easily expand their AI capabilities as their needs grow. Unlike traditional centralized AI systems, which may require significant upgrades to accommodate increased data volumes or processing demands, edge AI can scale horizontally by deploying extra edge devices. This enables smooth integration of new devices into existing infrastructure, ensuring efficient and cost-effective scalability without sacrificing performance or reliability.
Cost Effective
Edge AI reduces costs by processing data locally on devices, minimizing the need for expensive cloud infrastructure and reducing data transmission costs. This approach also eliminates the need for constant internet connectivity, further reducing operational network expenses.
Power Efficiency
Edge AI conserves power by conducting computations locally, minimizing data transmission over the network, and reducing reliance on energy-intensive cloud servers. Additionally, edge devices are designed to be more energy-efficient and sustainable compared to their cloud counterparts. This approach also leads to an improved carbon footprint for edge devices by reducing the number of I/O operations required by cloud AI applications.
Enhanced Data Security
Edge AI enhances data security by processing sensitive information locally on devices, reducing the risk of data exposure or attacks during transmission to cloud servers. This approach ensures that critical data remains within the confines of the device, mitigating the threat of unauthorized access, data breach, or interception.
Less Bandwidth Usage
Edge AI minimizes bandwidth usage by processing data locally on devices, reducing the volume of data that needs to be transmitted to the cloud for analysis. This results in optimized network bandwidth utilization, lower data transfer costs, and reduced network congestion, particularly in environments with limited bandwidth availability.
Reduced Latency
Edge AI also significantly reduces latency by processing data locally on devices. Just like the bandwidth, it eliminates the need to send data to remote cloud servers. This enables real-time decision-making and responsiveness, making Edge AI ideal for applications requiring immediate action, such as autonomous vehicles and industrial automation.
Real-time Performance
Edge AI enables high-performance computing directly on local devices, processing data, conducting instantaneous analysis, and running Machine Learning and Deep Learning algorithms. Unlike cloud processing, edge data processing takes milliseconds, making it ideal for critical real-time applications like defect detection in production lines and abnormal behavior detection in security systems.
Our products for high-performance computing: Edge AI Intelligence Solutions & Edge AI Jetson Platforms
On-Device Learning
Edge AI supports on-device learning, allowing devices, like microcontrollers, to continuously improve their performance and adapt to changing environments without requiring constant access to cloud-based resources. This enhances the intelligence and autonomy of edge devices by enabling autonomous operations and personalized user experiences.
Improved Privacy
Edge AI operations conduct most data processing locally, minimizing data sent to external locations or remote cloud servers, reducing the risk of data mishandling. This enhances privacy, which is vital for sensitive data like security, IP, and other private information.
High Availability for Devices
Edge AI ensures high availability for devices by enabling them to operate autonomously without relying on continuous internet connectivity or cloud-based services. This means that even in remote or disconnected environments where network connectivity may be limited or unreliable, edge-based applications can continue to function effectively. Reducing dependency on external resources, Edge AI can ensure uninterrupted functionality and responsiveness, vital for mission-critical applications.
How Does Edge AI Technology Work?

Edge AI devices follow a systematic process to analyze data and make real-time decisions at the edge of the network. Let’s take a look:
- Data Acquisition: Sensor devices capture raw data from the surrounding environment.
- Data Preprocessing: Raw data is preprocessed locally to filter noise and extract relevant features.
- Edge AI Inference: Preprocessed data is fed into the Edge AI model deployed on the edge device.
- Local Decision-Making: The Edge AI model analyzes the data and makes real-time decisions or predictions locally.
- Action: Based on the inference results, immediate actions or responses are triggered, enhancing efficiency and responsiveness.
In essence, Edge AI technology brings intelligence closer to the source of data, unlocking new possibilities for real-time operation and automation in all industries.
Edge AI Applications and Examples
Edge AI is revolutionizing industries by enabling real-time decision-making across sectors like infrastructure, healthcare, retail, energy, and agriculture, enhancing efficiency and sustainability in each field. Let’s learn more about the details!
Predictive Maintenance and Process Optimization in Manufacturing
Edge AI transforms manufacturing with predictive maintenance and process optimization in smart factories. AI-equipped edge devices analyze sensor data to detect equipment failures and defects in real time, enhancing productivity and minimizing downtime. This proactive approach to maintenance, particularly in IIoT devices, allows for early anomaly detection and predictive analysis, ensuring efficient production processes and cost savings through timely repairs.
A practical example is Advantech's edge AI solution for a semiconductor manufacturer, which implemented smart sensors and power meters across its facilities to monitor energy usage remotely. This integration resulted in approximately 10% energy savings and contributed to reducing carbon emissions.
Another example is the challenge of improving the PCB inspection process, which struggled with low AOI accuracy. Advantech partnered with Phison, implementing an AI-based system using deep learning for defect detection. This solution boosted first pass yield from 74% to 99%, cutting reinspection by 96%.
Patient Monitoring in Healthcare
In healthcare, edge AI enhances patient care by enabling real-time monitoring, diagnosis, and personalized treatment. Wearable devices, like cardiac trackers and blood pressure sensors, embedded with AI algorithms continuously monitor vital signs and analyze data locally, allowing for immediate detection of health issues and timely intervention. Also, with machine learning, AI systems swiftly and accurately examine medical images. This capability assists in spotting diseases at early stages, allowing for timely interventions that can potentially save lives and enhance treatment effectiveness.
Infrastructure and Traffic Management for Smart City
With Edge AI, smart cities can achieve efficient infrastructure management, traffic optimization, and public safety. Embedded within urban infrastructure like traffic lights and surveillance cameras, edge devices analyze data locally to identify traffic congestion, monitor environmental conditions, and track suspicious objects for enhanced security. This localized processing enables cities to improve public services, enhance safety measures, and allocate resources effectively, ultimately fostering a more sustainable and livable urban environment.
A great example of this is how Advantech's edge AI solution streamlines traffic management by using AI systems to analyze video data in real-time, adjusting traffic lights adaptively. This reduces deployment costs, improves data collection, and enables predictive traffic management and smarter infrastructure planning, ensuring smoother traffic flow and better decision-making for urban environments.
Smart Home Technology
Smart home devices with edge AI capabilities, such as voice-controlled appliances and video doorbells, process data locally, improving privacy and personalization without relying on the cloud. For example, a smart fridge can track food consumption and expiry, providing timely alerts.
Similarly, edge AI enhances home security by analyzing video streams from pre-installed cameras in real time. It distinguishes true intrusions from false alarms, sending notifications with video directly to your phone, and eliminating the need for expensive sensors or installations. These technologies enhance privacy, security, and overall living quality.
Retail Industry
In the retail market, edge AI drives customer engagement and enhances inventory management through real-time analysis of shopper behavior. Smart shelves with edge AI sensors optimize product placement and offer personalized promotions, improving operational efficiency and reducing costs. Additionally, voice ordering enhances the digital shopping experience, allowing shoppers to search for items, request product information, and place orders using smart devices.
For example, Amazon Go revolutionizes the shopping experience by using AI technologies like computer vision and sensor fusion, allowing customers to grab items and leave without checkout lines. Items are tracked and charged automatically upon exit. Furthermore, Alexa enhances convenience by using AI to process voice commands, enabling users to search for products, add items to carts, and place orders hands-free, further simplifying the retail experience.
Energy Management and Forcast
In the energy sector, Edge AI optimizes distribution, predicts demand, and enhances grid efficiency by combining historical data, weather patterns, and energy health data. Smart grids use AI-equipped edge devices to analyze real-time data, detect anomalies, and optimize energy flow, reducing waste and improving reliability. This ensures cost savings and sustainable infrastructure, which is critical for continuous energy supply and efficient resource management.
Let’s look at wind turbine maintenance, for example. The AI-powered visual inspection system addresses key challenges in this issue, offering real-time, cost-effective monitoring without downtime. By integrating AI models for detecting issues like cracks, ice accretion, and lightning strikes, the system ensures prompt repairs and optimal turbine performance. The solution is easy to install, retrofittable on existing turbines, and able to remotely monitor blade conditions through AI image recognition.
Agriculture
Edge AI is transforming agriculture by enabling real-time data processing on farms, allowing for instant decision-making to optimize operations. By utilizing sensors and cameras, edge AI systems monitor critical factors like soil conditions, weather, and crop growth, offering actionable insights for precise irrigation, fertilization, and pest control. This technology helps increase crop yields while reducing waste and environmental impact, making farming more efficient and sustainable.
In addition, advanced AI solutions are addressing labor shortages and improving fruit production. Automated fruit-picking systems use AI and robotics to identify and harvest mature fruit 24/7, boosting efficiency and cutting labor costs. The AI-powered Fruit Yield Prediction System further enhances farming by using deep learning and hyperspectral imaging to analyze fruit conditions. This allows farmers to make informed decisions about harvesting, marketing, and resource management, streamlining operations and increasing profitability. Together, these AI innovations are reshaping modern agriculture.
Farm Management
Edge AI is transforming farm management by enabling real-time decision-making directly on-site. It processes data from sensors and cameras at the source, allowing farmers to monitor soil health, irrigation needs, and crop conditions without relying on cloud services. This leads to more efficient water use, better crop management, and improved sustainability, ultimately optimizing agricultural productivity.
Let’s look at a real example: Advantech’s AI-powered solutions are transforming both shrimp and dairy farming by using real-time data analysis to enhance efficiency and sustainability. In shrimp farming, our edge AI system monitors health, behavior, and environmental conditions, optimizing feeding and reducing waste, which improves feed conversion ratios and environmental impact. Similarly, in dairy farming, the system uses AI and infrared vision to detect elevated livestock body temperatures, allowing early identification of sick animals and preventing disease spread. Both technologies improve food security and offer future expansion for monitoring growth and environmental factors, promoting sustainable farming practices.
Self-Driving Vehicles
Edge AI plays a crucial role in the development of self-driving vehicles by enabling real-time data processing at the source, such as within the vehicle itself. This localized processing allows autonomous vehicles to quickly analyze sensor data, detect road conditions or objects, and make immediate driving decisions without relying on cloud servers. By reducing latency and improving response times, edge AI enhances safety and efficiency, making self-driving cars more capable of navigating complex traffic situations and improving overall transportation systems.
Advantech's edge AI system significantly enhanced Turing Drive's self-driving bus operations by improving system stability, reducing communication costs, and addressing hardware integration challenges. This collaboration led to fewer system interruptions and optimized vehicle performance, helping Turing Drive expand its autonomous driving technology.
Future of Edge AI
As we gaze into the future of Edge AI, a realm of limitless possibilities unfolds. With advancing technology, Edge AI algorithms are poised to tackle complex tasks with remarkable efficiency. The convergence of factors like neural network maturity, IoT proliferation, and 5G technology leads us into a new era, setting the stage for enterprises in every field to seamlessly integrate AI and use real-time insights to enhance operations across sectors. The future of Edge AI holds incredible promise, bringing innovations that will reshape industries and fuel unprecedented growth.
To explore the exciting possibilities of Edge AI and witness its real-world applications in action, check out Advantech’s remarkable success stories in deploying Edge AI solutions. Experience the transformative power of Edge AI and unlock new opportunities for your business. Step into the future today with Advantech!
Frequently Asked Questions About Edge AI
1. Which industries benefit most from Edge AI?
Industries such as manufacturing, healthcare, agriculture, automotive, smart cities, and retail benefit significantly from Edge AI. Its applications include predictive maintenance, patient monitoring, real-time traffic management, autonomous vehicles, and personalized customer experiences.
2. How does Edge AI contribute to sustainability?
Edge AI reduces energy consumption by minimizing data transmission to cloud servers and leveraging energy-efficient edge devices. This leads to lower carbon emissions and supports sustainable operations in industries such as agriculture and manufacturing.
3. What are the main challenges of implementing Edge AI?
Implementing Edge AI comes with challenges such as limited computational power on edge devices, high initial costs for developing and deploying AI models, and ensuring interoperability across different devices. Additionally, maintaining data security and privacy while enabling seamless integration with existing infrastructure requires robust solutions.
4. Is Edge AI suitable for small businesses?
Yes, Edge AI can benefit small businesses by offering cost-effective, scalable solutions for automation, real-time analytics, and enhanced customer experiences. Edge devices with pre-trained AI models make it accessible without requiring extensive technical expertise.
5. What types of devices typically use Edge AI?
Common devices include IoT sensors, smartphones, wearables, autonomous vehicles, surveillance cameras, industrial robots, and smart home appliances. These devices leverage AI to enable real-time processing and decision-making at the edge.