Model-Compression

Edge AI: Intelligence on the Edge Edge AI describes running artificial intelligence directly on devices, gateways, or nearby servers instead of sending data to a central cloud. It uses smaller models and efficient hardware to process inputs where data is created. This approach speeds decisions, protects privacy, and keeps services available even with limited connectivity. What is Edge AI? It blends on-device inference with edge infrastructure. The goal is to balance accuracy, speed, and energy use. By moving computation closer to the data source, you can act faster and more reliably. ...

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. ...