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A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks
Conference proceeding   Open access

A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks

Murat Isik, Ankita Paul, M. Lakshmi Varshika and Anup Das
PROCEEDINGS OF THE 19TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS 2022 (CF 2022), pp 169-172
01 Jan 2022
DOI: https://doi.org/10.1145/3528416.3530232
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
http://arxiv.org/abs/2204.02942View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Computer Science, Theory & Methods Science & Technology Computer Science Technology
We propose a design methodology to facilitate fault tolerance of deep learning models. First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and synapse circuitries in each neuromorphic core are enclosed with astrocyte circuitries, the star-shaped glial cells of the brain that facilitate self-repair by restoring the spike firing frequency of a failed neuron using a closed-loop retrograde feedback signal. Next, we introduce astrocytes in a deep learning model to achieve the required degree of tolerance to hardware faults. Finally, we use a system software to partition the astrocyte-enabled model into clusters and implement them on the proposed fault-tolerant neuromorphic design. We evaluate this design methodology using seven deep learning inference models and show that it is both area- and power-efficient.

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10 citations in Web of Science
14 citations in Scopus

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