Automatic Kernel Optimization for Deep Learning on All Hardware Platforms

Optimizing the performance of deep neural network on a diverse range of hardware platforms is still a hard problem for AI developers. In terms of system support, we are facing a many-to-many problem here: deploying trained models from multiple frontends (e.g. Tensorflow, ONNX, MXNet) to multiple hardware platforms (e.g.