Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement! TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It aims to close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends. TVM provides the following main features:
Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet into minimum deployable modules on diverse hardware backends.
Infrastructure to automatic generate and optimize tensor operators on more backend with better performance.
In short, TVM to deep learning is kind of like LLVM to programming languages.