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The Vivante ACUITY Toolkit offers both command line tools and APIs, enabling seamless deployment of 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.

Table of Contents

Table of Contents
stylenone

1. Prerequisites

1.1. Host System Requirements

The host meets the requirements as described in Section 1.4, System Requirements following table outlines the essential system prerequisites for running the ACUITY Toolkit:

Resource

Note

CPU

Intel® CoreTM Core™ i5-6500 CPU@3.2GHz x4 with support of the Intel® Advanced Vector Extensions.

GPU

(Optional) NVIDIA® GPU cards with CUDA® architectures. A compatible version of CUDA and cuDNN is installed for TensorFlow

RAM

Minimum 8 GB at least

Disk

Minimum 160 GB

OS

Ubuntu 22.04 LTS 64-bit with Python 3.10,

Ubuntu 20.04 LTS 64-bit with Python 3.8 (recommended), or

Ubuntu 18.04 LTS 64-bit with Python 3.6

Note: Other Ubuntu versions are not recommended.

1.2. Python Requirements

Supported Python setups include:

  • Ubuntu 22.04 with Python 3.10

...

  • Ubuntu 20.04 with Python 3.8

...

  • Ubuntu 18.04 with Python 3.6

...

  • acuity-toolkit-6.18.1

Code Block
tensorflow==2.10.0
scipy
networkx==1.11
lmdb==0.93
onnxoptimizer==0.3.1
onnx==1.12.0
dill==0.2.8.2
ruamel.yaml==0.15.81
ply==3.11
torch==1.5.1

  • acuity-toolkit-6.21.1

Code Block
tensorflow==2.12.0
scipy
networkx==1.11
lmdb==0.93
protobuf==3.20.3
onnxoptimizer==0.3.13
onnx==1.14.0
dill==0.2.8.2
ruamel.yaml==0.15.81
ply==3.11
torch==1.5.1

1.3. VivanteIDE

The VivanteIDE is installed correctly if you want to generate NBG cases from the ACUITY Toolkit. Please refer to this document: VivanteIDE user guide

2. Binary Version

2.1. Install Procedure

...

1.3. Vivante IDE

The Vivante IDE should be installed correctly if you want to generate NBG cases from the ACUITY Toolkit. Please refer to this document: VivanteIDE Install

2. Version Information

To ensure seamless deployment of neural network models on edge 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

6.30.7

5.10.1

3. Installing ACUITY Binary Version

3.1. Extraction

To extract the binary package of the ACUITY toolkit, follow these steps:

  1. Extract the ACUITY package 'Verisilicon_Tool_Acuity_Toolkit_<version>.tgz, ' to a destination directory. There are two versions of the toolkit, Please confirm the version that needs to be installed:

    • acuity-toolkit-binary-6.18.1

    • acuity-toolkit-binary-6.21.1

    your desired destination directory.

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

...

3.2.

...

Verification

...

  1. Switch Navigate to the './acuity-toolkit-binary-<version>/bin' directory.

  2. Set the environment variable 'ACUITY_PATH as follows, which will used to execute the ACUITY binary tools

  3. Execute binary file

...

  1. ' to point to the bin directory:

    Code Block
    export ACUITY_PATH=<directory of acuity-toolkit-binary-

...

  1. version>/bin

  2. Execute the ACUITY binary tools using the command line. For example:

    Code Block
    yang.luo@ti-d630:~/c3v/acuity-toolkit-binary-6.21.1/bin$ ./pegasus 
usage: pegasus [-h] {import,export,generate,prune,inference,quantize,train,dump,measure,help} ...

...

  1. 
Pegasus commands.

...

  1. 
positional arguments:
  {import,export,generate,prune,inference,quantize,train,dump,measure,help}
    import              Import models.
    export              Export models.
    generate            Generate metas.
    prune               prune models.
    inference           Inference model and get result.
    quantize            Quantize model.
    train               Train model.
    dump                Dump model activations.
    measure             Get amount of calculation, parameter and activation.
    help                Print a synopsis and a list of commands.

...

  1. 
optional arguments:
	-h, --help						show this help message and exit

...

4. Installing ACUITY Wheel Version

...

4.1.

...

Extraction and Installation

  1. Extract the ACUITY package, Verisilicon‘Verisilicon_Tool_Acuity_Toolkit_<version>.tgztgz’, to a destination directory. There are two versions of the toolkit, Please confirm the toolkit version matches the Python version that needs to be installed：

    acuity-toolkit- whl-6.18.1-python-xxx

  2. acuity-toolkit- whl-6.21.1-python-xxx

  3. From this directory, locate and unpack the binary package ACUITY Wheel package ‘Vivante_acuity_toolkit_whl_<version>_python<version>’.

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

    Code Block
    pip3 install ./acuity-*.whl

4.2 Installing Tensorflow

The Wheel version of ACUITY requires a Tensorflow installation. Use the following commands to install the used Tensorflow version.

4.2.1. ACUITY Toolkit 6.18.1
Code Block
pip3 install tensorflow==2.10.0
pip3 install scipy
pip3 install networkx>=1.11
pip3 install lmdb==0.93
pip3 install onnxoptimizer==0.3.1
pip3 install onnx==1.12.0
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
4.2.2. ACUITY Toolkit 6.21.1
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

...

6.30.7
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

4.3. Verification

  1. Navigate to the './acuity-toolkit-whl-<version>/bin' directory.

  2. Execute python filethe Python file. For example:

    Code Block
    user@linux:~/c3v/acuity-toolkit-whl-6.21.1-python3.8.10/bin$ python pegasus.py -h
usage: pegasus [-h] {import,export,generate,prune,inference,quantize,train,dump,measure,help} ...

Pegasus commands.

positional arguments:
  {import,export,generate,prune,inference,quantize,train,dump,measure,help}
    import              Import models.
    export              Export models.
    generate            Generate metas.
    prune               prune models.
    inference           Inference model and get result.
    quantize            Quantize model.
    train               Train model.
    dump                Dump model activations.
    measure             Get amount of calculation, parameter and activation.
    help                Print a synopsis and a list of commands.

optional arguments:
  -h, --help            show this help message and exit