Half-precision inference in TensorFlow Lite's XNNPack backend has doubled the performance of on-device machine learning models by utilizing FP16 floating-point numbers on ARM CPUs. This advancement allows AI features to be deployed on older and lower-tier devices by reducing storage and memory overhead compared to traditional FP32 computations. The FP16 inference, now widely supported across mobile devices and tested in Google products, delivers significant speedups for various neural network architectures. Users can leverage this improvement by providing FP32 models with FP16 weights and metadata, enabling seamless deployment across devices with and without native FP16 support. This matters because it enhances the efficiency and accessibility of AI applications on a broader range of devices, making advanced features more widely available.
