Yuwei Hu

TL;DR: TVM as discussed by the authors is a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends, such as mobile phones, embedded devices, and accelerators.

TL;DR: TVM is proposed, an end-to-end optimization stack that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends and discusses the optimization challenges specific toDeep learning that TVM solves.

TL;DR: TVM is a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends and automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations.

TL;DR: In this article, the authors propose outlier channel splitting (OCS) which duplicates channels containing outliers and then halves the channel values, so that the network remains functionally identical, but affected outliers are moved toward the center of the distribution.

TL;DR: This work proposes outlier channel splitting (OCS), which duplicates channels containing outliers, then halves the channel values, and shows that OCS can outperform state-of-the-art clipping techniques with only minor overhead.