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Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Tallinn University of Technology, Tallinn, Estonia.
Tallinn University of Technology, Tallinn, Estonia.
Tallinn University of Technology, Tallinn, Estonia.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Tallinn University of Technology, Tallinn, Estonia.
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2023 (English)In: AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigorous reliability requirements on DNNs. Quantized Neural Networks (QNNs) have emerged to tackle the complexity of DNN accelerators, however, they are more prone to reliability issues.In this paper, a recent analytical resilience assessment method is adapted for QNNs to identify critical neurons based on a Neuron Vulnerability Factor (NVF). Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part.The method is validated by experiments on different QNNs and datasets. The results demonstrate that the proposed method for correcting the faults has a twice smaller overhead than a selective Triple Modular Redundancy (TMR) while achieving a similar level of fault resiliency. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023.
Keywords [en]
Neurons, Safety engineering, Critical neurons, Fault resilience, Human lives, Neural-networks, Novel methods, Performance, Reliability requirements, Safety critical applications, Splittings, Vulnerability factors, Deep neural networks
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-63960DOI: 10.1109/AICAS57966.2023.10168633Scopus ID: 2-s2.0-85166374493ISBN: 9798350332674 (print)OAI: oai:DiVA.org:mdh-63960DiVA, id: diva2:1788380
Conference
5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023, Hangzhou 11 June 2023 through 13 June 2023
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-08-16Bibliographically approved

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Daneshtalab, Masoud

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