Result for 0A0CC38B0DFD13615034DF15C6EF5B65147B17E4

Query result

Key Value
FileName./usr/share/doc/libxir1/changelog.Debian.gz
FileSize170
MD5C570A6AA223D8903897F448D4FBB8F64
SHA-10A0CC38B0DFD13615034DF15C6EF5B65147B17E4
SHA-25646291A94FCC7329771350E456FADA9D6F137568099D3A14EB04AC55516E10B74
SSDEEP3:FtteSK27bOFrLH93NkI+6kLwHlRbIKpb+wmphOaH0+uAT9j12tZLpf1PCNHn114:XtQ2/QLd3v+PLwHlRbIKpawMhOCxuAZu
TLSHT151C080552D46CE7BC70546758410BE71F554100E45F55C9B19F2951191FC87652C50F5
hashlookup:parent-total3
hashlookup:trust65

Network graph view

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
FileSize17396
MD53A96DC102098768170E1637348AF93DD
PackageDescriptionXilinx 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.
PackageMaintainerPunit Agrawal <punit@debian.org>
PackageNamelibxir-dev
PackageSectionlibdevel
PackageVersion1.3.2-1
SHA-12A88486F4FAF700028A887D157013A954287B836
SHA-2563B898ED22D9FAC3566F9E2368380F26F8D65458AEF6A1F355E9A6EC9672E1C28
Key Value
FileSize50588
MD5D50DA741DA314C374F90F523AD3B78C9
PackageDescriptionXilinx 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.
PackageMaintainerPunit Agrawal <punit@debian.org>
PackageNamelibxir-utils
PackageSectionutils
PackageVersion1.3.2-1
SHA-167412CCE5439B1C545280D6B822EA2347A066A55
SHA-2565FD2F8774B729DE89CDEB78A9EB06EF961C8E6F1314F864666F32BB4AE25B858
Key Value
FileSize1559272
MD52D4E1C36F0C231909FE52F6667DCFD13
PackageDescriptionXilinx 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.
PackageMaintainerPunit Agrawal <punit@debian.org>
PackageNamelibxir1
PackageSectionlibs
PackageVersion1.3.2-1
SHA-147DC45868DC2BC53D8DDD15E3A7F54A3EE3495C5
SHA-256B00C4CBE0B7F0F9B415D0C5A7ED1260E90231FFEE58BB4BC9D094F38B57695A5