Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

This is the code repository for the paper

Rapid learning with phase-change memory-based in-memory computing through learning-to-learn
Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Spela Brglez, Thomas Limbacher, Enrique Pinero, Alejandro Linares Barranco, Angeliki Pantazi, Robert Legenstein
ArXiv Link

Setup

You need Tensorflow to run this code. We used Python 3.9 and TensorFlow 2.5. See the corresponding Conda environment.yml file to install all necessary dependencies:

conda env create --file=environment.yml --name RapidLearningInMemoryComputing
conda activate RapidLearningInMemoryComputing
pip install --no-warn-conflicts -r requirements.txt

Usage

Few-shot image classification with PCM-based neuromorphic hardware

To start training on the few-shot image classification task, run

cd few_shot_image_classification
python main.py --seed 1234 --batch_size=32 --hidden_channels=56 --noise=False

To load an existing checkpoint, run

cd few_shot_image_classification
python main_omniglot.py --checkpoint checkpoints/pretrained-weights.pickle --seed 42  --batch_size=1 --hidden_channels=56 --noise=False --dataset_seed=128

Rapid online learning of robot arm trajectories in biologically-inspired neural networks

To start training on the robotic arm online learning task, run

cd online_learning_robot
python main.py

To load an existing checkpoint, run

cd online_learning_robot
python main.py --checkpoint checkpoints/pretrained-weights.pickle

Acknowledgements

This work was funded in part by the CHIST-ERA grant CHIST-ERA-18-ACAI-004, by the Austrian Science Fund (FWF) [10.55776/I4670-N], by grant PCI2019-111841-2 funded by MCIN/AEI/ 10.13039/501100011033, by SNSF under the project number 20CH21_186999 / 1 and by the European Union. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This work was supported by NSF EFRI grant #2318152. E. P.-F. work was supported by a "Formación de Profesorado Universitario" Scholarship, with reference number FPU19/04597 from the Spanish Ministry of Education, Culture and Sports. Furthermore, we thank the In-Memory Computing team at IBM for their technical support with the PCM-based NMHW as well as the IBM Research AI Hardware Center. Moreover, we thank Joris Gentinetta for his help with the setup for the robotic arm experiments.