Result for CC3D4AEF9D12938190A6BFE60804D860321005AA

Query result

Key Value
FileName./usr/include/xir/util/tool_function.hpp
FileSize5556
MD57914885138B050B9A59AC558DF380725
SHA-1CC3D4AEF9D12938190A6BFE60804D860321005AA
SHA-25675871D4D8E3B10DA67EB73446164D0AFE82E792334A8ADD3C416FA6778C733CD
SSDEEP96:G4y/XHFCmyED93PGR6UiZDp6814G9hzuUKeGC0QHPKok62nqTmkymLmom5oq:GvXHJ+RHiRp6814GOQHiok626mkymLhe
TLSHT102B1427434BD7F215A0214B6A5DEA0C2580A8599B33B5BE2F0DF14B0AF867694236F53
hashlookup:parent-total5
hashlookup:trust75

Network graph view

Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize17780
MD588C9A20600F39D062C5079AE08057D8B
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
PackageVersion1.4-1
SHA-1EAFA2210DB1574B4A819219B5073C852B7B5C448
SHA-256BD35110F67DE170B0E02860B6942AD28ADD2F8D5B51FD983F8BA9E06C4EE7FC2
Key Value
FileSize17784
MD5F495DEA2EF315F714778A5DB2BBB1C5B
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
PackageVersion1.4-1
SHA-171CAAD514F16B585AECA7FD835365F9B07C14589
SHA-2563B69F59FFBDADA50346035841C72EC04FC79F8228C29B0028BF518FF81985A90
Key Value
FileSize17792
MD534448452CD36D29A180283B5674B9C10
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
PackageVersion1.4-1
SHA-1F885C353E29C716A972B96AE0DA9059D51D48F47
SHA-256F56447C4F86D5E5110315893313648889917A793E6DBACA8F04BE70D820F5D00
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
FileSize17780
MD56CBD745A71FDB3AFF5EEF8CD5D77F1A7
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
PackageVersion1.4-1
SHA-1E06549795FB614B3B0555275202F0A37F48D1ABB
SHA-2569E8CE57F35ECFA912F906E672437205C9A95A965C0E81890CFCCB558B2D16D38