PFS - DNN Hardware Reliability Assessment and Enhancement
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hdl:2117/369987
Document typeConference report
Defense date2022-05
Rights accessRestricted access - publisher's policy
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Abstract
Emergence of Deep Neural Networks (DNN) has led
to a proliferation of artificial intelligence applications. Although
applications of DNNs to real-world problems have become
ubiquitous, there is a lack of understanding of how these circuits
are affected by faults. Due to this fact, adoption of DNNs in
safety-critical domain has been lagging behind. As there exist
no commonly accepted reliability assessment metrics for DNNs,
their certification for safety-critical applications is not possible.
In this PhD thesis, we are optimising the fault injection process
that is a widely used reliability assessment technique for DNNs.
CitationTaheri, M. DNN Hardware Reliability Assessment and Enhancement. A: 27th IEEE European Test Symposium (ETS). 2022,
Publisher versionhttps://ieeexplore.ieee.org/xpl/conhome/9810327/proceeding
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