What is edge AI?

Edge artificial intelligence (AI), or AI at the edge, is the use of AI in combination with edge computing to allow data to be collected at or near a physical location. For example, an image recognition algorithm task will run better being closer to the source of the data.

Edge AI allows responses to be delivered almost instantly. With edge AI, data is processed within milliseconds providing real-time feedback with or without internet connection because AI algorithms can process data closer to the location of the device. This process can be more secure when it comes to data because sensitive data never leaves the edge.

Edge AI differs from traditional AI because instead of running AI models at the backend of a cloud system, they run on connected devices operating at the network edge. This adds a layer of intelligence where the edge device not only collects metrics and analytics but is able to act upon them via an integrated machine learning (ML) model within the edge device.

The goal of artificial intelligence is the same – to have computers collect data, process that data, and then generate results similar to human intelligence. However, edge AI does the work and decision making locally, inside, or near whatever device being used. 

What is AI inference? 

How is edge AI different from distributed AI?

Related to edge AI is distributed AI which uses concepts from both traditional AI and edge AI. The main distinctions between the 2 appear where data is processed and how the AI models are deployed. Where edge AI runs algorithms directly on edge devices, distributed AI uses multiple interconnected systems–central servers, edge devices, and others.

In distributed AI, tasks are divided among several machines or devices, and each works on a portion of the problem. While this distributed processing can apply more power to the processing of data and is scalable beyond the capacity of edge AI, the trade-offs appear in its complexity, latency, and overall privacy.

How does cloud computing empower edge AI?

It is no exaggeration to claim that edge AI could not exist without cloud computing. Cloud computing provides the infrastructure, tools, and services to develop, deploy, manage, and maintain AI models on edge devices.

Training: Because edge AI devices exist away from centralized servers, they typically lack the computational power and large volumes of data necessary to train deep learning models. Edge AI devices instead transfer their data to a cloud where it is combined with that of similar devices, processed, and used to train the model. Trained machine learning models are then re-deployed to devices on the edge.

Deployment: Because edge devices are minimal by design, trained models need to be optimized for those resource-limited edge devices. Cloud services provide compression tools for quantizing and pruning that prepare AI models for deployment to the edge.

Data sync: Edge AI devices are able to process data quickly at their point of deployment. Edge AI devices also collect data in order to train their models to make better decisions. Edge AI devices regularly sync with a central repository in the cloud, which helps with storing and processing the data that the edge device is collecting and computing. Data sent to the cloud is used in continuous learning where the models are trained and re-deployed to the devices.

Monitoring and management: Edge AI devices are at the forefront of an organization’s interaction with their users. Cloud platforms monitor edge devices in real-time, enabling predictive maintenance and identifying potential issues before they impact performance. Additionally, cloud platforms can scale as needed offering elastic resources for an organization managing a fleet of devices.

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The combination of edge computing and artificial intelligence comes with great benefits. With edge AI, high-performance computing capabilities are brought to the edge, where sensors and IoT devices are located. Users can process data on devices in real time because connectivity and integration between systems isn’t required, and they can save time by collecting data, without communicating with other physical locations.

The benefits of edge AI include: 

• Less power use: Save energy cost with local data processes and lower power requirements for running AI at the edge compared to cloud data centers
• Reduced bandwidth: Reduce the amount of data needed to be sent and decrease costs with more data processed, analyzed, and stored locally instead of being sent to the cloud
• Privacy: Lower the risk of  sensitive data getting out with data being processed on edge devices from edge AI
• Security: Prioritize important data transfer by processing and storing data in an edge network or filtering redundant and unneeded data
• Scalability: Easily scale systems with cloud-based platforms and native edge capability on original equipment manufacturer (OEM) equipment 
• Reduced latency: Decrease the time it takes to process data on a cloud platform and analyze it locally to allow other tasks

Learn about the stages of AI at the edge

The benefits of edge AI allow for use cases across a variety of industries.

Edge AI in healthcare

Wearable devices such as smartwatches and fitness trackers can use edge AI to monitor vital signs (heart rate, oxygen levels) in real-time, alerting users to irregularities such as arrhythmias or high stress levels without relying on cloud processing. AI-enabled diagnostic tools can assist in medical imaging by analyzing X-rays, MRIs, and other medical scans at the “edge” of a hospital or clinic network, providing instant results and reducing the need to send data to central servers. Edge AI assists in remote patient monitoring of conditions at home, analyzing data from medical devices and alerting healthcare providers in real-time.

Manufacturing uses of edge AI

In manufacturing plants, edge AI can perform predictive maintenance, monitoring equipment in real-time for performance anomalies to predict mechanical failures. Cameras and sensors equipped with AI can enhance quality control by inspecting production lines for defects in products. Processing visual or sensory data locally, rather than in a remote central server, enables immediate corrections which can minimize waste. Edge AI-powered robotics and automation in factories can sort, package, or assemble, using real-time data from sensor inputs to adjust to environmental changes or product variability.

Edge AI at work in smart homes

In our everyday life, we’ve become accustomed to using voice assistants in the home to control lights, thermostats, and music. These devices use edge AI to process commands locally which reduces latency. Processing commands locally without sending to a central server also enhances privacy. Security systems integrated with smart doorbell and household cameras use edge AI to detect motion, recognize faces, and alert homeowners to unusual activity. Processing locally avoids the need to send continuous video streams to the cloud which improves both privacy and efficiency. Other smart home devices like thermostats use edge AI in energy management. They learn user behavior and local data to optimize heating/cooling schedules and reduce energy consumption.

Retail uses of edge AI

In retail, “smart shelves” employ edge AI for inventory management. Cameras and other sensors detect when items are out of stock or misplaced and notify staff to make adjustments. Many retailers are experimenting with checkout-free stores where edge AI systems track the products that customers select or return in real-time by processing data directly from in-store sensors and cameras.

Edge AI powers vehicles and traffic

Autonomous vehicles are themselves edge AI devices which rely on real-time data from sensors like cameras, LIDAR, and radar to navigate roads, detect obstacles, and make split-second decisions instead of relying on cloud connections. Smart traffic lights and cameras use edge AI for traffic management by analyzing traffic patterns in real-time which can help to reduce congestion and improve safety at intersections. Edge AI also optimizes the fleet management of logistics companies by monitoring vehicle performance, driver behavior, and optimizing delivery routes.

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