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Machine learning model deployment for training and execution has been animportant topic for industry and academic research in the last decade. Much ofthe attention has been focused on developing specific toolchains to supportacceleration hardware. In this paper, we present IREE, a unified compiler andruntime stack with the explicit goal to scale down machine learning programs tothe smallest footprints for mobile and edge devices, while maintaining theability to scale up to larger deployment targets. IREE adopts a compiler-basedapproach and optimizes for heterogeneous hardware accelerators through the useof the MLIR compiler infrastructure which provides the means to quickly designand implement multi-level compiler intermediate representations (IR). Morespecifically, this paper is focused on TinyIREE, which is a set of deploymentoptions in IREE that accommodate the limited memory and computation resourcesin embedded systems and bare-metal platforms, while also demonstrating IREE'sintuitive workflow that generates workloads for different ISA extensions andABIs through LLVM.

TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment