  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$\times$ in the majority of cases.
  Furthermore, our scheme dramatically reduces NAS search time. Code is
available
at~\href{https://github.com/jack-willturner/nas-as-program-transformation-exploration}{this
https url}.
