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This is the repository for the code associated with the paper titled "In-memory factorization of holographic perceptual representations"

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In-memory factorization

This is the repository for the code associated with the paper titled "In-memory factorization of holographic perceptual representations"

Authors: Michael Hersche her@zurich.ibm.com, Geethan Karunaratne kar@zurich.ibm.com

Installing Dependencies

You will need a machine with a CUDA-enabled GPU and the Nvidia SDK installed to compile the CUDA kernels. Further, we have used conda as a python package manager and exported the environment specifications to environment.yml. You can recreate our environment by running

$ conda create --name IMFEnv python=3.6
$ conda activate IMFEnv

Install pytorch CUDA and other requirements

$ (IMFEnv) conda install pytorch=1.3 torchvision cudatoolkit=10.1 -c pytorch
$ (IMFEnv) pip install -r requirements.txt

Run tests

To run the factorizer execute the following command from the root of the repository.

$ (IMFEnv) python main_capacity.py --custom-config experiments/<Experiment>/config.json

Available experiments are:

Experiment Description
100a_baseline Baseline factorizer
100b_dense Standard factorizer
100e_totnoise Factorizer total noise sweep
100e_prnoise Factorizer programming noise sweep
100e_rdnoise Factorizer read noise sweep

The script saves a .npz file which you can load after running and plot the accuracy vs. problem size.

Citation

@Article{langenegger2023imfactorizer,
    Author = {Langenegger, Jovin and Karunaratne, Geethan and Hersche, Michael and  Benini, Luca and Sebastian, Abu and Rahimi, Abbas },
    Journal = {Nature Nanotechnology},
    Year = {2023},
    Title = {Constrained Few-shot Class-incremental Learning},
    Year = {2022}}

License

Our code is licensed under Apache 2.0. Please refer to the LICENSE file for the licensing of our code.

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This is the repository for the code associated with the paper titled "In-memory factorization of holographic perceptual representations"

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