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This content will become publicly available on June 27, 2024

Title: Astromorphic Self-Repair of Neuromorphic Hardware Systems

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.

 
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Award ID(s):
2031632
NSF-PAR ID:
10476215
Author(s) / Creator(s):
; ;
Publisher / Repository:
Association for the Advancement of Artificial Intelligence (AAAI)
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
37
Issue:
6
ISSN:
2159-5399
Page Range / eLocation ID:
7821 to 7829
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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