Definition
Edge AI involves deploying AI algorithms on endpoint devices rather than relying on centralized data centers or the cloud. These devices can vary from mobile phones to gateways and edge servers situated closer to the data source.
How Edge AI Technology Works
- Edge AI employs neural networks and deep learning to train models that precisely identify, categorize, and describe objects in data.
- Initially, model training typically occurs in a central data center or the cloud due to the large amount of data required.
- Post-deployment, edge AI models improve gradually over time.
- If the AI encounters issues, the problematic data is sent to the cloud for further training.
- The refined model replaces the inference engine at the edge, improving performance.
Edge AI Benefits
- Low latency: The AI can provide real-time insights by deploying data directly to devices.
- Privacy and Security: Minimizes risk of data loss or comprise since data isn’t sent to centralized data centers.
- Bandwidth efficiency: Conserves bandwidth since data doesn’t need to travel to a central server back and forth.
- Operational continuity: Devices can work in the event of a network connection loss, which is helpful in applications like medical devices and autonomous vehicles.
- Energy efficiency: Edge devices consume less energy.
Edge AI Use Cases
- Self-driving vehicles: Edge AI enables autonomous cars to process data and make instant driving decisions.
- Smart cameras: These cameras use Edge AI to analyze images in real-time without sending the data to a central server.
- Healthcare: Wearables devices can detect data variations and instantly trigger an alert.
- Agriculture: Smart farming equipment uses edge AI to analyze soil conditions, detect pests and diseases, and optimize irrigation.