Researchers accelerate sparse inference on XNNPack and TensorFlow Lite for realtime apps
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 pow...
Source: neowin.net