Astromorphic Self-Repair of Neuromorphic Hardware Systems

• URL: http://arxiv.org/abs/2209.07428v1
• Date: Thu, 15 Sep 2022 16:23:45 GMT
• Title: Astromorphic Self-Repair of Neuromorphic Hardware Systems
• Authors: Zhuangyu Han, Nafiul Islam, Abhronil Sengupta
• Abstract summary: This paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults.
• Score: 0.8958368012475248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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