Source: armnn Section: devel Priority: optional Maintainer: Francis Murtagh Uploaders: Wookey , Emanuele Rocca Build-Depends: dpkg-dev (>= 1.22.5), libboost-test-dev (>= 1.64), libboost-system-dev (>= 1.64), libboost-filesystem-dev (>= 1.64), libboost-log-dev (>= 1.64), libboost-program-options-dev (>= 1.64), cmake, debhelper-compat (= 12), valgrind, libflatbuffers-dev, libarm-compute-dev [amd64 arm64 armhf], swig (>= 4.0.1-5), dh-python, python3-setuptools, libpython3-dev, python3-dev:any, python3-numpy:native, xxd, flatbuffers-compiler, chrpath Standards-Version: 4.6.2 Vcs-Git: https://salsa.debian.org/deeplearning-team/armnn.git Vcs-Browser: https://salsa.debian.org/deeplearning-team/armnn Package: libarmnn33t64 Provides: ${t64:Provides} Replaces: libarmnn33 Breaks: libarmnn33 (<< ${source:Version}) Architecture: any Multi-Arch: same Depends: ${shlibs:Depends}, ${misc:Depends} Suggests: libarmnntfliteparser24t64 (= ${binary:Version}), python3-pyarmnn (= ${binary:Version}) Description: Inference engine for CPUs, GPUs and NPUs - shared library Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the shared library package. Package: libarmnn-dev Architecture: any Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Inference engine for CPUs, GPUs and NPUs - header files Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the development package containing header files. Package: libarmnntfliteparser24t64 Provides: ${t64:Provides} Replaces: libarmnntfliteparser24 Breaks: libarmnntfliteparser24 (<< ${source:Version}) Architecture: any Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN TensorFlow Lite parser library Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the shared library package. Package: libarmnntfliteparser-dev Architecture: any Multi-Arch: same Depends: libarmnn-dev (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN TensorFlow Lite parser library - header files Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the development package containing header files. Package: python3-pyarmnn Architecture: any Depends: libarmnn33t64 (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends}, ${python3:Depends} Recommends: libarmnn-cpuref-backend33 Description: PyArmNN is a python extension for the Armnn SDK PyArmNN provides interface similar to Arm NN C++ Api. . PyArmNN is built around public headers from the armnn/include folder of Arm NN. PyArmNN does not implement any computation kernels itself, all operations are delegated to the Arm NN library. Package: libarmnn-cpuacc-backend33 Architecture: arm64 Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN dynamically loadable Neon backend Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the dynamically loadable Neon backend package. Package: libarmnnaclcommon33t64 Provides: ${t64:Provides} Replaces: libarmnnaclcommon33 Breaks: libarmnnaclcommon33 (<< ${source:Version}) Architecture: armhf arm64 Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN shared library for Arm Compute Library backends Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the common shared library used by Arm Compute Library backends. Package: libarmnn-cpuref-backend33 Architecture: any Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnnaclcommon33t64 (= ${binary:Version}) [arm64 armhf], ${shlibs:Depends}, ${misc:Depends} Description: Arm NN dynamically loadable Reference backend Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the dynamically loadable Reference backend package. Package: libarmnn-gpuacc-backend33 Architecture: armhf arm64 Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnnaclcommon33t64 (= ${binary:Version}) [arm64 armhf], ${shlibs:Depends}, ${misc:Depends} Description: Arm NN dynamically loadable CL backend Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is the dynamically loadable CL backend package. Package: armnn-latest-cpu Architecture: arm64 Multi-Arch: same Depends: libarmnn33t64(= ${binary:Version}), libarmnn-cpuacc-backend33 (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN Core and CPU backend Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This packaging release supports TensorFlow Lite. Arm NN takes networks from the framework, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is a dependency package containing the latest ArmNN Core package as well as the latest CPU backend and the TensorFlow Lite Parser. Package: armnn-latest-gpu Architecture: armhf arm64 Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnn-gpuacc-backend33 (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN Core and GPU backend Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This packaging release supports TensorFlow Lite. Arm NN takes networks from the framework, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is a dependency package containing the latest ArmNN Core package as well as the latest GPU backend and the TensorFlow Lite Parser. Package: armnn-latest-cpu-gpu Architecture: arm64 Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnn-cpuacc-backend33 (= ${binary:Version}), libarmnn-gpuacc-backend33 (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN Core, CPU and GPU backends Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This packaging release supports TensorFlow Lite. Arm NN takes networks from the framework, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is a dependency package containing the latest ArmNN Core package as well as the latest CPU backend, GPU backend and the TensorFlow Lite Parser. Package: armnn-latest-cpu-gpu-ref Architecture: arm64 Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnn-cpuacc-backend33 (= ${binary:Version}), libarmnn-gpuacc-backend33 (= ${binary:Version}), libarmnn-cpuref-backend33 (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN Core, CPU, GPU, and Reference backends Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This packaging release supports TensorFlow Lite. Arm NN takes networks from the framework, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is a dependency package containing the latest ArmNN Core package as well as the latest CPU backend, GPU backend, Reference backend and the TensorFlow Lite Parser. Package: armnn-latest-ref Architecture: any Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnn-cpuref-backend33 (= ${binary:Version}), libarmnntfliteparser24t64 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Arm NN Core and Reference backend Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This packaging release supports TensorFlow Lite. Arm NN takes networks from the framework, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is a dependency package containing the latest ArmNN Core package as well as the latest Reference backend and the TensorFlow Lite Parser. Package: armnn-latest-all Architecture: any Multi-Arch: same Depends: libarmnn33t64 (= ${binary:Version}), libarmnn-cpuacc-backend33 (= ${binary:Version}) [arm64], libarmnn-cpuref-backend33 (= ${binary:Version}), libarmnn-gpuacc-backend33 (= ${binary:Version}) [armhf arm64], libarmnntfliteparser24t64, ${shlibs:Depends}, ${misc:Depends} Description: Arm NN Core and all backends Arm NN is a set of tools that enables machine learning workloads on any hardware. It provides a bridge between existing neural network frameworks and whatever hardware is available and supported. On arm architectures (arm64 and armhf) it utilizes the Arm Compute Library to target Cortex-A CPUs, Mali GPUs and Ethos NPUs as efficiently as possible. On other architectures/hardware it falls back to unoptimised functions. . This packaging release supports TensorFlow Lite. Arm NN takes networks from the framework, translates them to the internal Arm NN format and then through the Arm Compute Library, deploys them efficiently on Cortex-A CPUs, and, if present, Mali GPUs. . This is a dependency package containing the latest ArmNN Core package as well as the latest CPU backend, GPU backend, Reference backend and the TensorFlow Lite Parser. CPU and GPU backends will only be installed on armhf or arm64 architectures.