Webnn

This handles the full lifecycle of browser-based neural network inference using the Web Neural Network API. You get navigator.ml feature detection, MLContext and MLGraphBuilder setup, explicit device selection with power preferences, tensor dispatch and readback, and graceful fallback paths when hardware acceleration isn't available. It won't help with server-side inference or cloud APIs. The procedures walk through identifying browser entry points, choosing between direct WebNN graphs or adapting existing runtimes like ONNX Runtime Web, and wiring up UX states for unsupported browsers versus actual accelerated execution. Useful when you need local ML inference that respects privacy constraints and can't phone home.