What is edge AI and how does it work?

The global edge AI market is expected to grow from $30 billion in 2026 to $118.7 billion in 2033, demonstrating its effect on how people’s devices and machines work.

When it comes to using edge AI in a commercial or industrial setting, it's important to understand what it is and how it works. This guide from Synaptics can help you learn more about edge AI and take advantage of its benefits.

Key Takeaways

• Edge AI refers to deploying AI applications on physical devices near the data's source.
• Internet of Things (IoT) devices that use edge AI can enhance data privacy and efficiency, compared to those that rely on cloud AI.
• Common components of edge AI include edge devices, sensors, gateways and servers.
• Edge AI is used in many industries, including manufacturing, healthcare and retail.

What Is Edge AI?

An edge AI deployment is when AI learning models and algorithms are deployed onto physical devices, such as smart cameras, embedded processors and IoT sensors. These local devices are considered to be at the edge of the network, compared to the data center or cloud facility at the network’s center. Deploying these AI applications locally enables real-time processing that doesn't constantly rely on a cloud infrastructure.

Edge AI combines two existing disciplines — edge computing, which is when data is processed near its source, and artificial intelligence, which enables devices to understand, classify and then act on that data.

How Does Edge AI Differ From Cloud AI?

While AI applications operate on physical devices in an edge deployment, cloud AI carries out these processes on remote servers. The data is sent via the cloud from local devices to the cloud data center, where the AI processes it, before the response is sent back to the devices at the network edge.

By processing data locally, edge AI can improve operational efficiency and promote stronger data protection.

Many systems use a hybrid model, where time-sensitive inference is handled by edge AI, while cloud AI covers large-scale model training and the more complex tasks.

How Does Edge AI Work?

Edge AI carries out two key processes, training and inference. Training is how an AI model learns and gains the ability to understand data input, while inference is how the AI model uses its training to make predictions and decisions based on previously unseen data. Understanding these terms is key to understanding how edge AI works.

1. Training AI Models in the Cloud

Before an AI model can be used, it must be trained to recognize patterns and classify objects through deep neural networking. Since training requires exposing the model to large datasets and significant computational resources, it usually happens in the cloud or in a centralized data center.

While this may initially seem like a disadvantage, it's actually an intentional design aspect that takes advantage of the cloud's larger capacity for data storage and computational demands.

2. Deploying Models to Edge Devices

Once the AI model can intelligently carry out its designated tasks, it's optimized through various techniques, including:

• Quantization: This process shrinks the model and speeds up inference so that it requires less memory, making it more suitable for edge devices.
• Pruning: By removing redundant or less critical connections and neurons from the neural network, pruning results in a network with fewer parameters and computations, leading to a smaller model.
• Knowledge distillation: The transfer of knowledge from a complex “teacher” model to a smaller and more efficient “student” model, which mimics the teacher's outputs, allows for a similar performance that requires fewer computational resources.

Once the model has been optimized, it can be deployed to the edge environment, where the model will operate within the edge devices' power, memory and compute limitations. These devices can vary from microcontroller units (MCUs) for always-on, low-power sensing to more powerful microprocessor units (MPUs) for complex vision or audio workloads.

3. Local Inference and Rapid Decision-Making

Once the AI model has been deployed locally to the edge devices, it will infer data locally. The model will analyze data coming from edge devices before producing an output without sending that data to the cloud. Real-world scenarios could include:

• A smart security camera detecting motion and classifying it as a person in real time.
• Your smartphone recognizing your face and unlocking.
• A smart speaker recognizing its “wake word” and readying itself to respond to your question or command.

This local inference is what allows edge AI to deliver low-latency performance and support data privacy.

4. The Feedback Loop and Continuous Improvement

While edge AI allows for more processes to be completed locally, that doesn't mean the system operates in isolation. Instead, when the model encounters an input that it's unsure how to classify, it will send the data back to the cloud for additional training. The updated and improved model is then sent back to the edge device.

Over time, this feedback loop allows the AI model to grow in confidence and accuracy, allowing it to store and process even more data locally. This continued learning is one of the key differences between edge AI and a static AI model.

What Are the Key Components of an Edge AI System?

Edge AI doesn't run on a single piece of hardware. Instead, it runs on an integrated system of components that work together:

• Edge devices and sensors: These devices capture data for the AI model to work with.
• Edge processors and AI accelerators: Silicon hardware such as neural processing units (NPUs) and graphics processing units (GPUs) optimize AI inferencing.
• Edge gateways: A device that acts as a networking layer between edge devices and the cloud. Edge gateways facilitate data transfer between the two.
• Machine learning models: These trained models run on the edge devices and infer data at its source.
• Edge servers: Consisting of a specialized computer or multiple computers, edge servers handle data processing, storage, security and networking. 

What Are the Benefits of Edge AI?

Edge AI can deliver tangible benefits to your Edge deployment, including:

Reduced Latency and Real-Time Processing

Since data is processed on a local device, edge AI often doesn't need to send data to a remote server and wait for a response, allowing for faster processing. This difference in processing speed is often beneficial, but it is critical for effective operations in some cases, such as:

• Production lines: Speed is key in automated manufacturing and assembly lines, which is why edge AI is often deployed to detect anomalies or defects and monitor product quality in real time. The delay caused by sending data to the cloud can result in thousands of defective products and significant material waste.
• Industrial equipment: Analyzing the vast amounts of data that industrial equipment often produces, edge AI can monitor the equipment's performance and condition in real time. This monitoring allows the AI to predict when maintenance is necessary and reduce the risk of a catastrophic breakdown.
• Autonomous vehicles: A self-driving car must be able to process data almost instantly, so that it can make safe and immediate decisions based on the road signs, vehicles, pedestrians and the road around it. Delays in processing could lead to road accidents.
• Portable medical devices: Devices such as smart wearables for cardiac monitoring and portable ultrasound machines can capture data and process it locally. This allows the devices to quickly provide information to medical professionals or the patient, enabling them to take the appropriate actions immediately and potentially avert a medical emergency. 

Enhanced Data Privacy and Security

While there are ways you can improve the security of your cloud AI, sending data from your edge devices to the cloud inevitably affects your data security. As your data is transmitted from your devices to the cloud, it can become exposed to interception and increase the data footprint across the network.

By keeping your data processing on your edge devices, you remove this additional risk. This reduced risk becomes particularly important when you're working with sensitive data, such as biometric inputs and health readings or private audio and video files. In many cases, this enhanced protection can also support compliance with data privacy regulations.

Lower Bandwidth and Operational Costs

Edge AI reduces data transmissions to and from the cloud by sending only relevant insights or inputs, reducing bandwidth consumption. Instead of continuously streaming raw, high-volume data, such as hours of video footage, edge AI processes the data locally. This local processing lowers the costs of cloud data transfers and can reduce cloud storage fees, since less data will need to be stored.

For organizations that use many IoT devices, the cost reduction can be significant.

Resilience and Offline Operation

While cloud AI requires network connectivity, edge AI can continue operating even when network connectivity is interrupted or unavailable because inference happens locally. This ability of edge AI is architecturally built in and can be particularly appealing in industrial environments or remote locations where a stable network connection can't be guaranteed.

What Industries Benefit Most from Edge AI Solutions?

Edge AI can be useful in many situations and environments, but some industries can particularly benefit from this technology.

Manufacturing and Industrial Automation

In the manufacturing and industrial automation industry, edge AI's key function is often to provide real-time quality control and anomaly detection. By processing the data locally, edge AI allows the machinery to respond quickly to any potential issues and halt operations.

It's also used in the production machinery to support timely predictive maintenance.

Healthcare and Remote Patient Monitoring

Edge AI enables real-time monitoring in wearable and portable medical devices, making it a vital technology to countless people every day. From people who rely on a wearable medical device to monitor their health, to anyone treated by an ambulance crew that uses smart diagnostic equipment and scanners, this technology can impact their health.

Beyond delivering faster results, edge AI also means that these devices don't require a network connection to operate. This ability greatly expands where and when they can be used, with technology like wearable devices gaining popularity in the United States.

Medical devices that use edge AI can also enhance data security, since the patient's health information isn't transmitted to the cloud. By keeping the data local, patients can enjoy increased privacy.

Retail and Customer Experience

Retailers are increasingly employing edge AI to assist with:

• Real-time inventory management: Smart sensors and cameras can use edge AI to monitor stock levels and provide timely alerts that inform the retailer when to replenish their stock.
• Loss prevention: Edge AI can be used in security systems to detect suspicious or unusual activity captured by the store's cameras. Behaviors like placing items into bags without scanning them or attempting walk-outs with unpurchased products can be quickly brought to the staff's attention to facilitate a fast response.
• Customer behavior analytics: Retailers can gain immediate insights from customer movements and interactions, allowing them to optimize the store without worrying about the costs of sending footage to the cloud.
• Checkout automation: The rapid processing that edge AI enables can facilitate a fast and frictionless checkout experience in “grab-and-go” stores. Cameras and sensors track which items customers put into their baskets as the AI tallies their virtual cart and processes their automatic payment when they walk out of the store.

Smart Home and Consumer IoT Devices

From smart speakers and thermostats to security cameras and washing machines, edge AI powers the intelligence in smart devices throughout the home. By processing inference locally on these devices, edge AI allows them to respond instantly to the conditions they were designed to recognize, be it a voice command, motion or user behavior, even when they don't have a network connection.

Local processing in consumer IoT devices also helps consumers protect sensitive household data, such as biometric inputs, audio and video.

The Impact of Edge AI

Edge AI's impact on how smart devices operate is undeniable. These devices and machines can be found in homes, places of work and even in what people wear. While they carry out similar functions to smart devices that rely on cloud AI, edge AI devices can deliver increased efficiency, lower data costs and enhanced data security.

This story was produced by Synaptics and reviewed and distributed by Stacker.