Accelerate Innovation with Integrated Edge AI Solutions

Edge AI refers to running artificial intelligence models directly on local devices (“the edge”) rather than relying solely on centralized cloud servers. These devices can include smartphones, sensors, IoT devices, cameras, robots and industrial machines. By processing data close to where it is generated, Edge AI enhances security and privacy while enabling enables faster, more efficient AI-powered decision-making.

Edge AI works by deploying trained machine learning models onto edge hardware. The process typically involves:

1. Data collection at the edge – Sensors, cameras or devices capture data in real time. Usage examples: it is used to enhance security of remote devices such as utility generators. It is also used in drones and other autonomous systems.
2. On-device inference – An AI model runs on the device using optimized frameworks.
3. Real-time decision-making – The device can classify, detect, predict and respond instantly without sending data to the cloud. This reduces bandwidth usage, lowers latency and improves reliability.
4. Optional cloud sync – Only necessary data such as summaries or alerts is sent to the cloud for storage, analytics or model updates.

Edge AI is used across many sectors that need real-time insights or operate in environments where cloud connectivity is unreliable or expensive, including:

• Smart Cities & Public Safety – Traffic monitoring, energy systems, security surveillance.
• Unmanned Autonomous Vehicles (UAVs) and Systems including Drones – Surveillance, anomaly detection, emergency alerts.

• Manufacturing & Industrial Automation – Predictive maintenance, defect detection, robotics control.
• Healthcare & Medical Devices – Portable diagnostic tools, patient monitoring, smart medical equipment.
• Automotive & Transportation- Autonomous vehicles, driver-assistance systems, fleet monitoring.
• Retail & Consumer Electronics- Smart cameras, cashier-less checkout, personalized customer analytics.
• Agriculture – Crop monitoring, livestock tracking, automated irrigation.
• Telecommunications – 5G networks enabling edge-based AI processing.
• Energy & Utilities – Grid management, remote monitoring of infrastructure.

✔ Ultra-low latency

Decisions are made instantly on the device, crucial for applications like autonomous driving or industrial robotics.

✔ Increased privacy & security

Raw data stays on-device, reducing exposure to cloud breaches and enhancing compliance with privacy regulations.

✔ Lower bandwidth & cloud costs

Only essential data is sent to the cloud, reducing data transfer and storage needs.

✔ Better reliability & offline capability

AI continues functioning even without internet connectivity—a key benefit for remote locations or mission-critical systems.

✔ Energy efficiency & scalability

Optimized edge Systems-on-Modules (SOMs) including those from Lantronix which are based on Qualcomm® processors, are designed to run AI efficiently at scale.

• Drones that analyze terrain in real time
• Smart cameras with built-in object detection
• Smartphones with on-device face recognition
• Wearables that detect health anomalies instantly
• Industrial sensors that predict machine failures

• On-device local processing
• Extremely low latency
• No connectivity required
• Higher privacy and security