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The Vivante ACUITY Toolkit offers both command line tools and APIs, enabling seamless deployment of machine learning neural network models onto devices with a neural processing unit (NPU). This toolkit facilitates the translation of model formats, network optimization, model training, quantization, and inference, until
final integrations with devices.

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To ensure seamless deployment of neural network models on SP7350 platformsedge devices, select compatible versions of the ACUITY Toolkit, Vivante IDE, and Linux NPU driver as listed below:

Linux NPU Driver

Vivante ACUITY Toolkit

Vivante IDE

v6.4.13.8

6.18.1

5.7.2

v6.4.15.9

6.21.1

5.8.2

v6.4.18.5

6.30.07

5.10.1

3. Installing ACUITY Binary Version

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  1. Extract the ACUITY package 'Verisilicon_Tool_Acuity_Toolkit_<version>.tgz' to your desired destination directory.

  2. From this directory, locate and unpack the binary package 'Vivante_acuity-_toolkit-_binary-_<version>.tgz'. Once unpacking is complete, the installation process is finished.

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  1. Extract the ACUITY package, ‘Verisilicon_Tool_Acuity_Toolkit_<version>.tgz’, to a destination directory.

  2. From this directory, locate and unpack the ACUITY Wheel package ‘acuity-toolkit-whl-<version>’ ‘Vivante_acuity_toolkit_whl_<version>_python<version>’.

  3. From the './acuity-toolkit-whl-<version>/bin' directory, select and install a Wheel package with the following commands:

    Code Block
    pip3 install ./acuity-*.whl

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Code Block
pip3 install tensorflow==2.12.0
pip3 install scipy
pip3 install networkx>=1.11
pip3 install lmdb==0.93
pip3 install protobuf<=3.20.3
pip3 install onnxoptimizer==0.3.13
pip3 install onnx==1.14.0
pip3 install dill==0.2.8.2
pip3 install ruamel.yaml==0.15.81
pip3 install ply==3.11
pip3 install torch==1.5.1
4.2.3 ACUITY Toolkit 6.30.

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7
Code Block
pip3 install tensorflow==2.15.0
pip3 install scipy
pip3 install networkx>=1.11
pip3 install lmdb==0.93
pip3 install protobuf<=3.20.3
pip3 install onnxoptimizer==0.3.13
pip3 install onnx==1.14.0
pip3 install dill==0.2.8.2
pip3 install ruamel.yaml==0.15.81
pip3 install ply==3.11
pip3 install torch==1.5.1

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