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Structures that represent and optimize parallel execution patterns within compiled models.
Distinct from Parallelizing Compilers: Defines the representation of parallel patterns in the model rather than the automated transformation of functional code.
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IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Defines structures to represent and optimize parallel execution patterns within a compiled machine learning model.