Ubuntu
xir package

xir 1.4-2 source package in Ubuntu

Changelog

xir (1.4-2) unstable; urgency=medium

  * Update d/control.
    Set arm64, armhf and amd64 supporting architecture.

 -- Nobuhiro Iwamatsu <email address hidden>  Thu, 21 Oct 2021 11:43:54 +0900

Upload details

Uploaded by:
Punit Agrawal
Uploaded to:
Sid
Original maintainer:
Punit Agrawal
Architectures:
amd64 arm64 armhf
Section:
misc
Urgency:
Medium Urgency

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File Size SHA-256 Checksum
xir_1.4-2.dsc 2.1 KiB f0bb4b7a9e9b967fc6ef0843dc210759ada9509e778e3651eba41356346f03d7
xir_1.4.orig.tar.xz 182.3 KiB ffbe7de5686608cbb8360f8764596195319794447dc1b6577ef0d6562bcab88a
xir_1.4-2.debian.tar.xz 5.4 KiB 7487c5d06997941ebce2af0c195af4915d41f4c1c29389d50865d73e52792ece

Available diffs

No changes file available.

Binary packages built by this source

libxir-dev: Xilinx Intermediate Representation (XIR) for deep learning algorithms (develop)

 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 provides the development environment for XIR.

libxir-utils: 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.

libxir-utils-dbgsym: debug symbols for libxir-utils
libxir1: Xilinx Intermediate Representation (XIR) for deep learning algorithms (runtime)

 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 provides the runtime environment for XIR.

libxir1-dbgsym: debug symbols for libxir1