3 accepted papers
Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space which lacks the ability to efficiently enable domain…
Search-based tensor compilers can greatly accelerate the execution of machine learning models by generating high-performance tensor programs, such as matrix multiplications and convolutions. These compilers take a high-level mathematical expression as input and search for the fastest low-level imple…
Several recently proposed architectures of neural networks such as ResNeXt, Inception, Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple branches and have demonstrated improved performance in many applications. We show that one cause for such success is due to t…