Edge AI: Running Models on Device
Edge AI: Running Models on Device Edge AI means running AI models directly on devices such as smartphones, cameras, or sensors. This avoids sending data to a remote server for every decision. On-device inference makes apps quicker, and it helps keep data private. It also works when the network is slow or unavailable. Benefits are clear. Privacy by design: data stays on the device. Low latency: responses come in milliseconds, not seconds. Offline resilience: operations continue without cloud access and with lower bandwidth use. To fit models on devices, teams use several techniques. Model compression reduces size. Quantization lowers numerical precision from 32-bit to 8-bit, saving memory and power. Pruning removes less important connections. Distillation trains a smaller model to imitate a larger one. Popular choices include MobileNet, EfficientNet-Lite, and other compact architectures. Run-times like TensorFlow Lite, PyTorch Mobile, and ONNX Runtime help deploy across different hardware. ...