Edge AI

Definitions

1. Running AI/ML inference directly on edge devices (phones, cameras, sensors, cars) rather than sending data to the cloud. Benefits: lower latency (real-time inference), privacy (data stays on device), offline capability, and reduced bandwidth. Requires model optimization (quantization, pruning, distillation) to fit resource constraints. Apple's Neural Engine and Google's Edge TPU are dedicated hardware.

   In plain English: Running AI models directly on devices (phones, cameras) instead of sending data to the cloud, for speed and privacy.

   Example: "The security camera runs object detection on its built-in Edge TPU -- it identifies people in 10ms without streaming video to the cloud."

Related Terms

• Inference
• Few-Shot Learning
• Synthetic Data
• Model Card
• Embedding Space
• Federated Learning
• Embedding Model
• Ablation Study
• Autoregressive Model