56 lines
2.3 KiB
Markdown
56 lines
2.3 KiB
Markdown
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# TIM-VX - Tensor Interface Module for OpenVX
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TIM-VX is a software integration module provided by VeriSilicon to facilitate deployment of Neural-Networks on OpenVX enabled ML accelerators. It serves as the backend binding for runtime frameworks such as Android NN, Tensorflow-Lite, MLIR, TVM and more.
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Main Features
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- Over [130 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
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- Simplified C++ binding API calls to create Tensors and Operations
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- Dynamic graph construction with support for shape inference and layout inference
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- Built-in custom layer extensions
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- A set of utility functions for debugging
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## Framework Support
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- [Tensorflow-Lite Delegate](https://github.com/VeriSilicon/tensorflow/tree/vx-delegate.v2.4.1) (Unofficial)
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- [Tengine](https://github.com/OAID/Tengine) (Official)
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- MLIR Dialect (In development)
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- TVM (In development)
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## Roadmap
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Roadmap of TIM-VX will be updated here in the future.
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## Get started
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### Build and Run
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TIM-VX uses [bazel](https://bazel.build) build system by default. [Install bazel](https://docs.bazel.build/versions/master/install.html) first to get started.
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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.
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To build TIM-VX
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```shell
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bazel build libtim-vx.so
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```
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To run sample LeNet
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```shell
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# set VIVANTE_SDK_DIR for runtime compilation environment
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export VIVANTE_SDK_DIR=`pwd`/prebuilt-sdk/x86_64_linux
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bazel build //samples/lenet:lenet_asymu8_cc
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bazel run //samples/lenet:lenet_asymu8_cc
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```
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To build and run Tensorflow-Lite delegate on A311D platform
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```shell
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# clone and cross build VeriSilicon tensorflow fork with TFlite delegate support
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git clone --single-branch --branch vx-delegate.v2.4.1 git@github.com:VeriSilicon/tensorflow.git vx-delegate; cd vx-delegate
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bazel build --config A311D //tensorflow/lite/tools/benchmark:benchmark_model
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# push benchmark_model onto device and run
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./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite --use_vxdelegate=true
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```
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