Package: libxir-utils (2.5-1 and others)

Xilinx Intermediate Representation (XIR) for deep learning algorithms (utils)

Xilinx Intermediate Representation (XIR) is a graph based intermediate representation of the AI algorithms which is well designed for compilation and efficient deployment of the Domain-specific Processing Unit (DPU) on the FPGA platform. Advanced users can apply Whole Application Acceleration to benefit from the power of FPGA by extending the XIR to support customized IP in Vitis AI flow.

XIR includes Op, Tensor, Graph and Subgraph libraries, which providing a clear and flexible representation for the computational graph. For now, it's the foundation for the Vitis AI quantizer, compiler, runtime and many other tools. XIR provides in-memory format, and file format for different usage. The in-memory format XIR is a Graph object, and the file format is a xmodel. A Graph object can be serialized to a xmodel while the xmodel can be deserialized to the Graph object.

In the Op library, there's a well-defined set of operators to cover the wildly used deep learning frameworks, e.g. TensorFlow, Pytorch and Caffe, and all of the built-in operators for DPU. This enhences the expression ability and achieves one of the core goals of eliminating the difference between these frameworks and providing a unified representation for users and developers.

XIR also provides a Python APIs which is named PyXIR. It enables Python users to fully access XIR and benefits in a pure Python environment, e.g. co-develop and integrate users' Python project with the current XIR based tools without massive dirty work to fix the gap between two languages.

This package contains the utilities from XIR.

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