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IREE exists in an ecosystem of projects, each using their own terminology and interacting in various ways. Below is a summation of these projects with the problems they are built to address.


JAX is a front-end for writing and executing Machine Learning models, including support for Google Cloud TPUs.


Multi Level Intermediate Representation is the compiler framework that IREE is built around. Beyond the tooling this includes a set of common dialects and transformations that IREE utilizes for its code generation system.

For general discussion on MLIR see the project's discourse group.


Linalg is an MLIR dialect that defines how Linear Algebra operations can be described in a generalized fashion, including a set of commonly used operations. IREE's code generation defines tensor operations using the Linalg dialect, then uses it to generate the loop structures for the CPU and GPU backends.


SPIR-V is a shader and kernel intermediate language for expressing parallel computation typically used for GPUs. It serves as a hardware agnostic assembly format for distributing complex, computationally intensive programs. It is the preferred method for shipping platform agnostic binaries to run on GPUs.


The TOSA specification defines a set of common tensor operations to most machine learning frameworks. This simplifies model compilation as separate front-end frameworks can target TOSA's intermediate representation without compromising on the ability to achieve efficient execution across multiple device types.

IREE uses the TOSA MLIR dialect as a prioritized ingestion format, transforming multiple ML-platform ingestion formats into a TOSA compatible set of operations.

Changes to the TOSA specification require submitting a proposal on TOSA's platform development page


TensorFlow lite is a model format and execution system for performing on device inference. IREE supports TFLite FlatBuffers translation into TOSA operations and compilation into the LinAlg dialect.