What is Edge AI and How Does It Work?

What is Edge AI? 

Edge AI, also known as “AI at the edge,” combines edge computing with artificial intelligence. Originally developed for content delivery networks (CDNs), edge computing is a distributed IT architecture in which data is stored at or near the source, enabling real-time, low-latency processing and analysis. Edge AI brings machine learning models into the mix, executing them on local devices at the data source and at the network edge rather than in the cloud or a distant data center, bypassing internet connectivity to deliver real-time results.

How Edge AI Works

It actually starts in the cloud, where ML models are built and trained to perform a specific task(s) using large language models (LLMs) and deep neural networks (DNNs). For example, creating a model to perform facial recognition on a smartphone camera and embedding it in the device.

Here’s a step-by-step explanation of what happens next with edge AI examples:

1. Collect data on the edge device, e.g., through a sensor in an autonomous vehicle, or a camera in a home security system
2. Clean, organize, and format the data on the local device to enable efficient processing. For example, reducing the image size from a camera
3. Then, the device uses the trained model to draw inferences from the data and make a prediction or decision, e.g., to detect a health issue via a wearable device
4. Based on the decision or prediction, the device then takes immediate action, e.g., a user and a home security company receive an alert that someone is trying to break in
5. This is where the cloud comes back into play. The model will send important information to the cloud for various purposes, such as model improvement and logging

Edge AI vs. Cloud AI: Key Differences

While edge AI and cloud AI intersect regularly, here are some of the ways they are different:

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Top Benefits of Edge AI

Edge AI is particularly beneficial to end users who need quick solutions to real-life problems. The technology can even be life-saving. For example, a smart wearable device for people with diabetes can alert them in real time when their blood sugar levels are low.  

The top benefits of edge AI applications include:

• Real-time responses: By eliminating network latency, edge AI devices respond faster by processing data and making decisions locally. For example, in smart cities, if a gas leak occurs, local sensors can alert emergency managers and citizens immediately to prevent disasters or even loss of life.
• Better security and privacy: Sensitive data is more vulnerable to cyberattacks when it travels over the network. Edge AI processes sensitive information on the device or a highly secure local network to reduce the risk of compromise.
• Lower costs: Processing and storing data locally reduces network overhead, cloud storage, and data transfer costs. Current edge AI examples show that it makes more economic sense 
• High availability: By decentralizing data processing and enabling offline functionality, critical systems become more resilient during network outages. For example, local edge AI devices can help workers on an oil rig maintain safety protocols during emergencies.
• System intelligence: AI can process a wide range of unstructured data, including text, speech, video, audio, temperature readings, and more. Devices and applications without edge AI capabilities can’t respond quickly to diverse inputs in complex situations.

Did you know? Early results show that simulation-led testing combined with secure edge AI can significantly reduce downtime and support faster decision-making, driving greater operational efficiency and quicker response times. (Source: Deloitte)

Challenges and Limitations of Edge AI

Every technology has its downsides and limitations. While edge AI has many advantages, here are some of its current limitations:

• Energy consumption: AI requires significant power to operate, especially with more complex models. Many edge AI devices, such as drones, wearables, and IoT sensors, are battery-powered, so the Machine learning models that power them must be highly optimized to operate effectively.
• Hardware capabilities: Edge AI devices often have lower compute, memory, and storage resources, making it more difficult to build models that can run on the hardware itself.
• Privacy and compliance: While there are advantages to processing and storing data locally on edge devices, they remain vulnerable to compromise. They must include robust security and authorization features to maintain privacy and comply with regulations such as GDPR and HIPAA.
• Model size limitations: Edge devices lack the capacity to run large, complex models, so they must be compressed or reworked entirely to run properly.
• Systems integration: Connecting edge AI with legacy enterprise systems, cloud platforms, and networks is not a simple feat.

Future Trends in Edge AI

Edge AI is still in its early days, and it’s not slowing down. Here are some of the edge AI developments in the works:

• Small Language Models (SLMs): Designed to run on edge devices, these AI models have simpler architectures. Unlike large language models (LLMs), these are trained on highly specialized data to run specific tasks.
• Quantization: This is a mathematical approach that enables faster processing on edge devices, LLM compression, analog-to-digital conversion, and lower power consumption for AI.
• Hybrid models: As seen in the evolution of cloud computing, hybrid models are often more powerful than a single approach. Expect to see more strategic, industry-specific integrations between cloud and edge AI deployments, including federated learning across edge devices and hybrid split inference techniques.
• Hardware advancements: Semiconductor companies are developing new chips purpose-built for edge AI computing operating in constrained environments. OEMs are building devices with edge AI processing capabilities.
• 5G, 6G, and MEC: New 5G and 6G network capabilities, along with multi-access edge computing (MEC), are enabling advanced edge architectures and AI processing at local cell towers rather than in cloud data centers.

Key Takeaways

• Edge AI and cloud computing will always be intertwined with complementary features
• Industry-specific use cases will create new versions of edge AI applications
• Companies are developing new technologies to enable edge AI at scale
• Hybrid cloud and edge AI architectures will emerge as a dominant strategy

FAQs

1. What is the difference between edge AI and distributed AI?

Edge AI refers to machine learning models that run at the data source. Distributed AI refers to AI that is run across multiple connected systems and nodes.

• Edge AI example: A home security camera that detects an intruder and sends only the relevant frames to a central system later
• Distributed AI example: Many interconnected cameras and servers to analyze activity across a smart city

2. What industries use edge AI the most?

The industries currently using edge AI the most include (but aren’t limited to) healthcare, manufacturing, retail, smart homes, and security and surveillance. For example, the retail industry uses it to create personalized in-store experiences that match online experiences through features such as smart shopping carts equipped with sensors. In healthcare, one use case is wearable edge AI devices that alert caretakers when a patient falls. 

3. Edge AI vs. fog computing—what’s the difference?

While edge AI runs on the device where data is collected, fog computing refers to an architecture in which storage, networking, and data processing occur in an intermediate layer between the edge and the cloud. That “fog” layer is closer to the edge than the cloud, reducing latency and only sending relevant data to the cloud.

4. What are some of the top edge AI companies?

The top companies in the edge AI space span hardware (including semiconductors and processors), software, and cloud providers. Hardware companies include NVIDIA, AMD, Intel, Qualcomm, and Hailo. Edge Impulse provides a specialized software platform for building and deploying edge AI models. In the cloud, AWS and Microsoft Azure offer cloud-to-edge AI services, including AWS Edge Services and Azure IoT Edge.

5. What are some examples of edge AI in IoT devices?

Edge AI is the backbone of all IoT devices. A great edge AI example in IoT can be found in the agricultural industry, where edge-enabled sensors analyze field or equipment data to make better-informed decisions about irrigation, crop monitoring, or machine maintenance.

6. What is the future of edge AI in 5G?

Edge AI will run on nearby 5G-connected nodes, enabling lower latency, faster processing, and enhanced 5G security compared to the cloud. In addition, 5G networks will use edge AI technology to offer “self-healing networks” that can manage traffic, improve performance, reduce energy usage, and automate security.