AnalogAINas is a modular and flexible framework to facilitate implementation of Analog-aware Neural Architecture Search. It offers high-level classes to define: the search space, the accuracy evaluator, and the search strategy. It leverages the aihwkit framework to apply hardware-aware training with analog non-idealities and noise included. AnalogAINAS obtained architectures are more robust during inference on Analog Hardware. We also include two evaluators trained to rank the architectures according to their analog training accuracy.

While installing the repository, creating a new conda environment is recomended.

git clone https://github.com/IBM/analog-nas/
pip install -r requirements.txt 
pip setup.py install 

To get started, check out nas_search_demo.py to make sure that the installation went well.

This python script describes how to use the package.