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. CPU, GPU, Accelerators). The most performance critical part of this problem is obtaining high performance kernel implementations for growing model architectures and hardware platforms.