Edge AI

Edge AI refers to technology that runs AI (artificial intelligence) processing on edge devices — such as sensors and terminals — rather than on cloud servers. "Edge" here means the periphery of a network, and typical edge devices include smartphones, surveillance cameras, IoT devices, drones, and robots. Conventionally, AI processing was performed on high-performance cloud servers. Edge AI, however, is designed so that devices can perform AI inference (prediction and classification) locally, without cloud connectivity. This makes it valuable in environments with unreliable internet connections, and it is attracting attention for its real-time performance and privacy protection benefits. Key advantages of Edge AI: • Real-time processing: Decisions are made locally without sending data to the cloud, reducing latency • Enhanced security and privacy: Data is processed on-device without being transmitted externally • Reduced communication costs: Eliminates the need to upload large volumes of data to the cloud • Offline capability: Continues operating even when network connectivity is unavailable Applications include pedestrian detection and suspicious behavior alerting from surveillance cameras, anomaly monitoring on manufacturing lines, crop health checks by agricultural drones, and real-time health monitoring from wearable devices. Recent advances in AI model compression (e.g., TinyML, quantization, distillation) and dedicated hardware (e.g., Edge TPU, NVIDIA Jetson, Apple Neural Engine) are accelerating the adoption of Edge AI. Edge AI is becoming the infrastructure for using AI "anytime, anywhere, quickly, and safely," and is expected to proliferate across virtually every industry sector.