Ubuntu
xir package

xir 1.4-2ubuntu1 source package in Ubuntu

Changelog

xir (1.4-2ubuntu1) jammy; urgency=medium

  * d/rules: add -Wno-error=deprecated-declarations to fix the build
    against OpenSSL 3.0

 -- Simon Chopin <email address hidden>  Tue, 07 Dec 2021 16:19:24 +0100

Upload details

Uploaded by:
Simon Chopin
Uploaded to:
Jammy
Original maintainer:
Ubuntu Developers
Architectures:
amd64 arm64 armhf
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Jammy release universe misc

Downloads

File Size SHA-256 Checksum
xir_1.4.orig.tar.xz 182.3 KiB ffbe7de5686608cbb8360f8764596195319794447dc1b6577ef0d6562bcab88a
xir_1.4-2ubuntu1.debian.tar.xz 5.6 KiB 30ae6aa4a8f7a34ca7909859f2320679272646f088a03af04ace3a9791e58105
xir_1.4-2ubuntu1.dsc 2.0 KiB f45cda19917fbe55a30d89af194240d2a46eb7f4f6bc9ed00037caac4b8756f6

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Binary packages built by this source

libxir-dev: No summary available for libxir-dev in ubuntu kinetic.

No description available for libxir-dev in ubuntu kinetic.

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: No summary available for libxir1 in ubuntu kinetic.

No description available for libxir1 in ubuntu kinetic.

libxir1-dbgsym: debug symbols for libxir1