Install AnalogVNN

AnalogVNN is tested and supported on the following 64-bit systems:

• Python 3.7, 3.8, 3.9, 3.10, 3.11

• Windows 7 and later

• Ubuntu 16.04 and later, including WSL

• Red Hat Enterprise Linux 7 and later

• OpenSUSE 15.2 and later

• macOS 10.12 and later

Installation

Install PyTorch then:

• Pip:

  # Current stable release for CPU and GPU
  pip install analogvnn
  
  # For additional optional features
  pip install analogvnn[full]
  

OR

• AnalogVNN can be downloaded at (GitHub) or creating a fork of it.

Dependencies

Install the required dependencies:

• PyTorch

  • Manual installation required: https://pytorch.org/

• dataclasses

• scipy

• numpy

• networkx

• (optional) tensorboard

  • For using tensorboard to visualize the network, with class analogvnn.utils.TensorboardModelLog.TensorboardModelLog

• (optional) torchinfo

  • For adding summary to tensorboard by using analogvnn.utils.TensorboardModelLog.TensorboardModelLog.add_summary()

• (optional) graphviz

  • For saving and rendering forward and backward graphs using analogvnn.graph.AcyclicDirectedGraph.AcyclicDirectedGraph.render()

• (optional) python-graphviz

  • For saving and rendering forward and backward graphs using analogvnn.graph.AcyclicDirectedGraph.AcyclicDirectedGraph.render()

That’s it, you are all set to simulate analog neural networks.

Head over to the Tutorial and look over the Sample code.