Result for 05DF9644520872129A40CBF3F4F850FA6232ED3C

Query result

Key Value
FileName./usr/share/doc/libxir1/copyright
FileSize2165
MD543214CC4B65CB65FBDF0071724ABDB72
SHA-105DF9644520872129A40CBF3F4F850FA6232ED3C
SHA-256E3F3CB7932C4FEA5DB20109DBACDDFD72BB72D06A630273CAE07502DF46CB11D
SSDEEP48:J/HA24OX0ehzH31cSn7HZD0Jvlm6Er7QH0s5ANU3oWF5J:Bg24gPzHFcSbImpHQHFo4
TLSHT17541A62E764407772BD157D1BE2EA8CEB31BB248741B5394645DC244533522F82FA460
hashlookup:parent-total15
hashlookup:trust100

Network graph view

Parents (Total: 15)

The searched file hash is included in 15 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
FileSize1615720
MD5A6F4B3B523908E6D6797AB08C969A70D
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
PackageVersion1.4-1
SHA-1DD91D9F0C7E03155834E1157DF65BA38D4A27104
SHA-25613A047273456C15FF69337A1B2F372A5E4D1089D088594CA0D6AE2C2422C838C
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
FileSize1385236
MD51BF05B55E68E9ABC905CA1407BEA1BF3
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
PackageVersion1.4-1
SHA-16ABA95513F5617EC59EBE134A673572BA2226999
SHA-256EC4FCE9B58C8946AF39D364BDFF0DBC850E47A8D692214A0D8B3EB953247443E
Key Value
FileSize47468
MD5BBD7E806D23CE21112B6BA05A27E2947
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
PackageVersion1.4-1
SHA-18E376AE4139C94C1257D95F1B39426EE9BAE9BFD
SHA-2564C7482427FA835B114549E8C4BB8071E5746A58229807FE9434C852306FF081B
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
FileSize1392012
MD59BC97215A7E7C3E22A77E833E6826AF3
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
PackageVersion1.4-1
SHA-1AC28530C3B8716794A8FB1A178A4D6C0E9BAC104
SHA-256DBFEAB876AEF976C375CBED9A503ABEE58430A7EBD1952A4D2AC93FB5DAD6702
Key Value
FileSize50536
MD5BF9EE5E7A8B1DFF3FFB135A54C749C66
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
PackageVersion1.4-1
SHA-1A6BBA1AE5F10E057E0BAE53C0B578E7E3E80664B
SHA-256619EBAE79CFACF928D7920BEC65E05C1FE6D84E880FA0A1A4266DDD3E549764F
Key Value
FileSize42452
MD5AEA38D85CB311ABB38D497C1BBF2766B
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
PackageVersion1.4-1
SHA-1A9B53D554637B7E101BE3692C04BB5939BC30B7B
SHA-256A83A2BA10832FC27A0AB07F831E7E0761542D6C32254510FE63EFDC28882B159
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
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
Key Value
FileSize41340
MD5EB546973CB25CB92D28DEBE2D27C80A8
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
PackageVersion1.4-1
SHA-1935B101B4770A3B1FF1470D0FD6023475A898CB7
SHA-2565C6350660B9D11C129DBEDF3D9AF4D6C4FD6883F8DFFE413033A216F12E58095
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
Key Value
FileSize1484936
MD5A20AF182198E4AA576424D5E5DDE066B
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
PackageVersion1.4-1
SHA-16758F7EBDBCAB886036A2DC5A380C16D9E35A308
SHA-25645E01A188216B8250D9D28FC6E5AC337953552992D64E38D484DE4342B44443E