A Hierarchical Fault-Tolerant and Cost Effective Framework for RRAM Based Neural Computing Systems

Vatti, Chandrasekhara Srinivas and M B, Rakesh and Acharyya, Amit and et al, . (2022) A Hierarchical Fault-Tolerant and Cost Effective Framework for RRAM Based Neural Computing Systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 69 (3). pp. 684-688. ISSN 1549-7747

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Abstract

An RRAM-based computing system (RCS) is widely used in neuromorphic computing systems due to its fast computation and low cost. The immature fabrication processes cause high rate of hard faults and limited endurance of RRAMs restrict the life of RCS. These can degrade the accuracy of RCS remarkably. The training of neural network consumes lot of time due to large network size and more number of layers. We propose a hierarchical fault-tolerant and cost effective framework for RCS. The proposed flow can tolerate the faults in the next training phase using the inherent fault-tolerant capability of the neural network without using extra hardware. Proposed framework consists of significant classification and remapping, transfer learning, threshold training and online fault detection. The proposed solution is validated and evaluated on CIFAR-10 dataset on VGG11 and Alexnet networks. Proposed solution resulted in training time improvements of 40% and number of RRAM writes are reduced by 97%. © 2004-2012 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amithttp://orcid.org/0000-0002-5636-0676
Item Type: Article
Uncontrolled Keywords: Deep learning; Fault tolerance; Neuromorphic computing system; Non-volatile memory; Resistive RAM; Stuck-at faults
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 20 Jul 2022 11:39
Last Modified: 20 Jul 2022 11:39
URI: http://raiith.iith.ac.in/id/eprint/9604
Publisher URL: http://doi.org/10.1109/TCSII.2022.3144193
OA policy: https://v2.sherpa.ac.uk/id/publication/3424
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