2021/02/12 by Jack Turner, Turner, Jack, Elliot J. Crowley +3 · 2 citations
Computer Science · Mathematics · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Programming Languages (cs.PL) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.2102.06599
openalex publication_date 2021/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations in order to exploit hardware parallelism and memory hierarchy. However, legality concerns mean they fail to exploit the natural robustness of neural networks. In contrast, NAS techniques mutate networks by operations such as the grouping or bottlenecking of convolutions, exploiting the resilience of DNNs. In this work, we express such neural architecture operations as program transformations whose legality depends on a notion of representational capacity. This allows them to be combined with existing transformations into a unified optimization framework. This unification allows us to express existing NAS operations as combinations of simpler transformations. Crucially, it allows us to generate and explore new tensor convolutions. We prototyped the combined framework in TVM and were able to find optimizations across different DNNs, that significantly reduce inference time - over 3× in the majority of cases. Furthermore, our scheme dramatically reduces NAS search time. Code is available at~\hrefhttps://github.com/jack-willturner/nas-as-program-transformation-explorationthis https url.