Ubuntu
xir package

xir 2.5-1build1 source package in Ubuntu

Changelog

xir (2.5-1build1) lunar; urgency=medium

  * Rebuild against new libprotobuf32.

 -- Gianfranco Costamagna <email address hidden>  Wed, 04 Jan 2023 17:51:06 +0100

Upload details

Uploaded by:
Gianfranco Costamagna
Uploaded to:
Lunar
Original maintainer:
Debian Xilinx Package Maintainers
Architectures:
amd64 arm64 armhf armel
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Mantic release universe misc
Lunar release universe misc

Downloads

File Size SHA-256 Checksum
xir_2.5.orig.tar.xz 193.4 KiB 113d9ee2929755212c6fbd9bf6187c60cbef58152e10add4e54345a625b4c501
xir_2.5-1build1.debian.tar.xz 6.1 KiB d6b12f0e3a88647db25dc5fdcf1c59342118544172c8e0a0d8c0cc32e9b3bfe7
xir_2.5-1build1.dsc 2.2 KiB b2d825aaacb6535782001e1645d687053712f5c558a3757b341405f89fe05217

Available diffs

View changes file

Binary packages built by this source

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

No description available for libxir-dev in ubuntu noble.

libxir-utils: No summary available for libxir-utils in ubuntu noble.

No description available for libxir-utils in ubuntu noble.

libxir-utils-dbgsym: debug symbols for libxir-utils
libxir2: 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.

libxir2-dbgsym: No summary available for libxir2-dbgsym in ubuntu noble.

No description available for libxir2-dbgsym in ubuntu noble.