Astromorphic Self-Repair of Neuromorphic Hardware Systems
DOI:
https://doi.org/10.1609/aaai.v37i6.25947Keywords:
ML: Bio-Inspired Learning, CMS: Other Foundations of Cognitive Modeling & SystemsAbstract
While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. The work investigates stronger correlations with astrocyte computational neuroscience models to develop macro-models with a higher degree of bio-fidelity that accurately captures the dynamic behavior of the self-repair process. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults in neuromorphic hardware systems with significantly better accuracy and repair convergence for unsupervised learning tasks on the MNIST and F-MNIST datasets. Our implementation source code and trained models are available at https://github.com/NeuroCompLab-psu/Astromorphic_Self_Repair.Downloads
Published
2023-06-26
How to Cite
Han, Z., Islam, A. N. M. N., & Sengupta, A. (2023). Astromorphic Self-Repair of Neuromorphic Hardware Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 37(6), 7821-7829. https://doi.org/10.1609/aaai.v37i6.25947
Issue
Section
AAAI Technical Track on Machine Learning I