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Partitions execution and data flow to coordinate operation scheduling across hardware targets.
Distinct from Execution Flow Control: Focuses on the coordination of tensor operation schedules across devices, not debugger flow control.
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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
Partitions execution and data flow to coordinate how operations are scheduled and dispatched across diverse hardware targets.