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Published in 2020 at "Microelectronics Reliability"
DOI: 10.1016/j.microrel.2020.113969
Abstract: Abstract Deep learning tasks cover a broad range of domains and an even more extensive range of applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural Network (DNN) algorithms are implemented on different systems,…
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Keywords:
errors dnn;
dnn accelerators;
review;
comprehensive review ... See more keywords
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Published in 2022 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2022.3141054
Abstract: Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous and led to an ever-increasing quest on adopting them from Cloud to edge computing. However, state-of-the-art DNN accelerators pack immense computational power in a…
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Keywords:
thermal aware;
mml;
dnn accelerators;
design ... See more keywords
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Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2022.3212645
Abstract: Current state-of-the-art employs approximate multipliers to address the highly increased power demands of deep neural network (DNN) accelerators. However, evaluating the accuracy of approximate DNNs is cumbersome due to the lack of adequate support for…
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Keywords:
fast emulation;
approximate;
dnn accelerators;
dnn ... See more keywords
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Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2021.3108415
Abstract: Deep Neural Network (DNN) accelerators are now ubiquitous. Extensive research is being directed at low power DNN accelerators for battery operated devices at the expense of a little drop in accuracy. These DNN accelerators have…
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Keywords:
methodology;
dnn accelerators;
test;
power ... See more keywords