Researchers accelerate sparse inference on XNNPack and TensorFlow Lite for realtime apps Mar 10, 2021 01:20 EST with 0 comments As proved by the Universal Approximation Theorem, neural networks can learn any arbitrary function. This allows us to capture hidden patterns in data to make more accurate and robust models for a wide variety of tasks. However, a big caveat in this process is that neural networks tend to grow quickly as the parameters (or complexity) of the task at hand increases. Naturally, these large neural networks require substantial computational power. To this end, researchers have been working towards optimizing large neural networks so that they can be run on smaller devices like smartphones and less powerful computers. Inference frameworks like TensorFlow Lite with XNNPACK ML acceleration library, specialize in this task. They optimize machine learning models to run on a variety of devices by finding a sweet spot between model size, inference speed, and accuracy. Building on this, today, Google released new features for the XNNPACK acceleration library and TensorFlow Lite that enable efficient inference of sparse networks.