Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	17396
MD5	3A96DC102098768170E1637348AF93DD
PackageDescription	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.
PackageMaintainer	Punit Agrawal <punit@debian.org>
PackageName	libxir-dev
PackageSection	libdevel
PackageVersion	1.3.2-1
SHA-1	2A88486F4FAF700028A887D157013A954287B836
SHA-256	3B898ED22D9FAC3566F9E2368380F26F8D65458AEF6A1F355E9A6EC9672E1C28

Key	Value
FileSize	50588
MD5	D50DA741DA314C374F90F523AD3B78C9
PackageDescription	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.
PackageMaintainer	Punit Agrawal <punit@debian.org>
PackageName	libxir-utils
PackageSection	utils
PackageVersion	1.3.2-1
SHA-1	67412CCE5439B1C545280D6B822EA2347A066A55
SHA-256	5FD2F8774B729DE89CDEB78A9EB06EF961C8E6F1314F864666F32BB4AE25B858

Key	Value
FileSize	1559272
MD5	2D4E1C36F0C231909FE52F6667DCFD13
PackageDescription	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.
PackageMaintainer	Punit Agrawal <punit@debian.org>
PackageName	libxir1
PackageSection	libs
PackageVersion	1.3.2-1
SHA-1	47DC45868DC2BC53D8DDD15E3A7F54A3EE3495C5
SHA-256	B00C4CBE0B7F0F9B415D0C5A7ED1260E90231FFEE58BB4BC9D094F38B57695A5