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
FileSize	17780
MD5	88C9A20600F39D062C5079AE08057D8B
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	1.4-1
SHA-1	EAFA2210DB1574B4A819219B5073C852B7B5C448
SHA-256	BD35110F67DE170B0E02860B6942AD28ADD2F8D5B51FD983F8BA9E06C4EE7FC2

Key	Value
FileSize	1615720
MD5	A6F4B3B523908E6D6797AB08C969A70D
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	1.4-1
SHA-1	DD91D9F0C7E03155834E1157DF65BA38D4A27104
SHA-256	13A047273456C15FF69337A1B2F372A5E4D1089D088594CA0D6AE2C2422C838C

Key	Value
FileSize	17784
MD5	F495DEA2EF315F714778A5DB2BBB1C5B
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	1.4-1
SHA-1	71CAAD514F16B585AECA7FD835365F9B07C14589
SHA-256	3B69F59FFBDADA50346035841C72EC04FC79F8228C29B0028BF518FF81985A90

Key	Value
FileSize	1385236
MD5	1BF05B55E68E9ABC905CA1407BEA1BF3
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	1.4-1
SHA-1	6ABA95513F5617EC59EBE134A673572BA2226999
SHA-256	EC4FCE9B58C8946AF39D364BDFF0DBC850E47A8D692214A0D8B3EB953247443E

Key	Value
FileSize	47468
MD5	BBD7E806D23CE21112B6BA05A27E2947
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	1.4-1
SHA-1	8E376AE4139C94C1257D95F1B39426EE9BAE9BFD
SHA-256	4C7482427FA835B114549E8C4BB8071E5746A58229807FE9434C852306FF081B

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	1392012
MD5	9BC97215A7E7C3E22A77E833E6826AF3
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	1.4-1
SHA-1	AC28530C3B8716794A8FB1A178A4D6C0E9BAC104
SHA-256	DBFEAB876AEF976C375CBED9A503ABEE58430A7EBD1952A4D2AC93FB5DAD6702

Key	Value
FileSize	50536
MD5	BF9EE5E7A8B1DFF3FFB135A54C749C66
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	1.4-1
SHA-1	A6BBA1AE5F10E057E0BAE53C0B578E7E3E80664B
SHA-256	619EBAE79CFACF928D7920BEC65E05C1FE6D84E880FA0A1A4266DDD3E549764F

Key	Value
FileSize	42452
MD5	AEA38D85CB311ABB38D497C1BBF2766B
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	1.4-1
SHA-1	A9B53D554637B7E101BE3692C04BB5939BC30B7B
SHA-256	A83A2BA10832FC27A0AB07F831E7E0761542D6C32254510FE63EFDC28882B159

Key	Value
FileSize	17792
MD5	34448452CD36D29A180283B5674B9C10
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	1.4-1
SHA-1	F885C353E29C716A972B96AE0DA9059D51D48F47
SHA-256	F56447C4F86D5E5110315893313648889917A793E6DBACA8F04BE70D820F5D00

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

Key	Value
FileSize	41340
MD5	EB546973CB25CB92D28DEBE2D27C80A8
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	1.4-1
SHA-1	935B101B4770A3B1FF1470D0FD6023475A898CB7
SHA-256	5C6350660B9D11C129DBEDF3D9AF4D6C4FD6883F8DFFE413033A216F12E58095

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	17780
MD5	6CBD745A71FDB3AFF5EEF8CD5D77F1A7
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	1.4-1
SHA-1	E06549795FB614B3B0555275202F0A37F48D1ABB
SHA-256	9E8CE57F35ECFA912F906E672437205C9A95A965C0E81890CFCCB558B2D16D38

Key	Value
FileSize	1484936
MD5	A20AF182198E4AA576424D5E5DDE066B
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	1.4-1
SHA-1	6758F7EBDBCAB886036A2DC5A380C16D9E35A308
SHA-256	45E01A188216B8250D9D28FC6E5AC337953552992D64E38D484DE4342B44443E