Tensor kernels in machine learning (ML) often correspond to pure mathematical expressions, making term rewriting an attractive strategy for optimization and mapping to specialized hardware accelerators. However, existing ML intermediate representations (IRs) tend to either be pure but high-level, making low-level rewrites to hardware targets inexpressible, or low-level but impure, hampering the use of term rewriting altogether.This paper introduces Glenside, a pure IR whose core abstraction-the access pattern-enables low-level, layoutaware, hardware-centric program rewrites. We demonstrate how term rewriting in Glenside can be used to map program fragments to hardware accelerator invocations and automatically discover classic data layout transformations like im2col. Glenside establishes a new foundation for exploring further term rewriting techniques in optimizing low-level tensor programs.CCS Concepts: • Software and its engineering → Domain specific languages; • Computing methodologies → Machine learning; • Theory of computation → Equational logic and rewriting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.