# TIM-VX **Repository Path**: mirrors/TIM-VX ## Basic Information - **Project Name**: TIM-VX - **Description**: TIM-VX 是 VeriSilicon 提供的一个软件集成模块，用于简化在 VeriSilicon ML 加速器上部署神经网络 - **Primary Language**: C/C++ - **License**: MIT - **Default Branch**: integration/opencv - **Homepage**: https://www.oschina.net/p/tim-vx - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2025-02-24 - **Last Updated**: 2025-07-09 ## Categories & Tags **Categories**: machine-learning **Tags**: None ## README # TIM-VX - Tensor Interface Module ![Bazel.VSim.X86.UnitTest](https://github.com/VeriSilicon/TIM-VX/actions/workflows/bazel_x86_vsim_unit_test.yml/badge.svg) ![CMake.VSim.X86.UnitTest](https://github.com/VeriSilicon/TIM-VX/actions/workflows/cmake_x86_vsim_unit_test.yml/badge.svg) TIM-VX is a software integration module provided by VeriSilicon to facilitate deployment of Neural-Networks on Verisilicon ML accelerators. It serves as the backend binding for runtime frameworks such as Android NN, Tensorflow-Lite, MLIR, TVM and more. Main Features - Over [150 operators](https://github.com/VeriSilicon/TIM-VX/blob/main/src/tim/vx/ops/README.md) with rich format support for both quantized and floating point - Simplified C++ binding API calls to create Tensors and Operations [Guide](https://github.com/VeriSilicon/TIM-VX/blob/main/docs/Programming_Guide.md) - Dynamic graph construction with support for shape inference and layout inference - Built-in custom layer extensions - A set of utility functions for debugging ## Framework Support - [Tensorflow-Lite](https://github.com/VeriSilicon/tflite-vx-delegate) (External Delegate) - [Tengine](https://github.com/OAID/Tengine) (Official) - [TVM](https://github.com/VeriSilicon/tvm) (Fork) - [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) (Official) - [OpenCV](https://github.com/opencv/opencv/pull/21036) (In development) - MLIR Dialect (In development) Feel free to raise a github issue if you wish to add TIM-VX for other frameworks. ## Architecture Overview ![TIM-VX Architecture](docs/image/timvx_overview.svg) # Get started ## Build and Run TIM-VX supports both [bazel](https://bazel.build) and [cmake](https://cmake.org). ---- ### cmake To build TIM-VX for x86 with prebuilt: ```shell mkdir host_build cd host_build cmake .. make -j8 make install ``` All install files (both headers and *.so) is located in : `host_build/install` cmake options: | option name | Summary | Default | | ----- | ----- | ----- | |`TIM_VX_ENABLE_TEST`| Enable unit test case for public APIs and ops | OFF | |`TIM_VX_ENABLE_LAYOUT_INFER`| Build with tensor data layout inference support| ON | |`TIM_VX_USE_EXTERNAL_OVXLIB`| Replace internal with a prebuilt libovxlib library | OFF | |`OVXLIB_LIB`|full path to libovxlib.so include so name, required if `TIM_VX_USE_EXTERNAL_OVXLIB`=ON | Not set | |`OVXLIB_INC`|ovxlib's include path, required if `TIM_VX_USE_EXTERNAL_OVXLIB`=ON| Not set | |`EXTERNAL_VIV_SDK`| Give external vivante openvx driver libraries | Not set| |`TIM_VX_BUILD_EXAMPLES`| Build example applications | OFF | |`TIM_VX_ENABLE_40BIT` | Enable large memory (over 4G) support in NPU driver | OFF | ---- run unit test: ```shell cd host_build/src/tim export LD_LIBRARY_PATH=`pwd`/../../../prebuilt-sdk/x86_64_linux/lib::$LD_LIBRARY_PATH export VIVANTE_SDK_DIR=`pwd`/../../../prebuilt-sdk/x86_64_linux/lib export VSIMULATOR_CONFIG= # if you want to debug wit gdb, please set export DISABLE_IDE_DEBUG=1 ./unit_test ``` #### Build with local google test source ```shell cd git clone --depth 1 -b release-1.10.0 git@github.com:google/googletest.git cd /build/ cmake ../ -DTIM_VX_ENABLE_TEST=ON -DFETCHCONTENT_SOURCE_DIR_GOOGLETEST= ``` ---- #### Build for your evk-board 1. prepare toolchain file follow cmake standard 2. make sure cross build low-level driver with toolchain separately, we need the sdk from the low-level driver 3. add -DEXTERNAL_VIV_SDK= to cmake definitions, also remember -DCMAKE_TOOLCHAIN_FILE= 4. or for using a buildroot toolchain with extrnal VIV-SDK add: -DCONFIG=BUILDROOT -DCMAKE_SYSROOT=${CMAKE_SYSROOT} -DEXTERNAL_VIV_SDK=${BUILDROOT_SYSROOT} 5. then make ---- #### Important notice for integration If you want to build tim-vx as a static library, and link it to your shared library or application, please be carefull with the linker, "-Wl,--whole-archive" is required. @see **samples/lenet/CMakeLists.txt** for reference ### Bazel [Install bazel](https://docs.bazel.build/versions/master/install.html) to get started. TIM-VX needs to be compiled and linked against VeriSilicon OpenVX SDK which provides related header files and pre-compiled libraries. A default linux-x86_64 SDK is provided which contains the simulation environment on PC. Platform specific SDKs can be obtained from respective SoC vendors. To build TIM-VX: ```shell bazel build libtim-vx.so ``` To run sample LeNet: ```shell # set VIVANTE_SDK_DIR for runtime compilation environment export VIVANTE_SDK_DIR=`pwd`/prebuilt-sdk/x86_64_linux bazel build //samples/lenet:lenet_asymu8_cc bazel run //samples/lenet:lenet_asymu8_cc ``` ## Other To build and run Tensorflow-Lite with TIM-VX, please see [README](https://github.com/VeriSilicon/tflite-vx-delegate#readme) To build and run TVM with TIM-VX, please see [TVM README](https://github.com/VeriSilicon/tvm/blob/vsi_npu/README.VSI.md) # Reference board Chip | Vendor | References :------ |:----- |:------ i.MX 8M Plus | NXP | [ML Guide](https://www.nxp.com.cn/docs/en/user-guide/IMX-MACHINE-LEARNING-UG.pdf), [BSP](https://www.nxp.com/design/software/embedded-software/i-mx-software/embedded-linux-for-i-mx-applications-processors:IMXLINUX?tab=Design_Tools_Tab) A311D | Khadas - VIM3 | [A311D datasheet](https://dl.khadas.com/Hardware/VIM3/Datasheet/A311D_Quick_Reference_Manual_01_Wesion.pdf), [BSP](https://dl.khadas.com/Firmware/VIM3/Ubuntu/EMMC/VIM3_Ubuntu-server-focal_Linux-4.9_arm64_EMMC_V0.9-20200530.7z) S905D3 | Khadas - VIM3L | [S905D3](https://dl.khadas.com/Hardware/VIM3/Datasheet/S905D3_datasheet_0.2_Wesion.pdf) , [BSP](https://dl.khadas.com/Firmware/VIM3L/Ubuntu/EMMC/VIM3L_Ubuntu-server-focal_Linux-4.9_arm64_EMMC_V0.9-20200530.7z) # Support create issue on github or email to ML_Support@verisilicon.com