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Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs

Mapping Transformation (MT) method to mitigate Stuck-at-Fault (SAF) defects from memristor-based DNNs.

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Abstract:
“When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make them faster, much more energy efficient, and accurate. Despite having excellent properties, the memristor-based DNNs are yet to be commercially available because of Stuck-At-Fault (SAF) defects. A Mapping Transformation (MT) method is proposed in this paper to mitigate Stuck-at-Fault (SAF) defects from memristor-based DNNs. First, the weight distribution for the VGG8 model with the CIFAR10 dataset is presented and analyzed. Then, the MT method is used for recovering inference accuracies at 0.1% to 50% SAFs with two typical cases, SA1 (Stuck-At-One): SA0 (Stuck-At-Zero) = 5:1 and 1:5, respectively. The experiment results show that the MT method can recover DNNs to their original inference accuracies (90%) when the ratio of SAFs is smaller than 2.5%. Moreover, even when the SAF is in the extreme condition of 50%, it is still highly efficient to recover the inference accuracy to 80% and 21%. What is more, the MT method acts as a regulator to avoid energy and latency overhead generated by SAFs. Finally, the immunity of the MT Method against non-linearity is investigated, and we conclude that the MT method can benefit accuracy, energy, and latency even with high non-linearity LTP = 4 and LTD = −4.”

Find the open access technical paper link here. Published Feb. 2022.

Oli-Uz-Zaman, M.; Khan, S.A.; Yuan, G.; Liao, Z.; Fu, J.; Ding, C.; Wang, Y.; Wang, J. Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs. J. Low Power Electron. Appl. 2022, 12, 10. https://doi.org/10.3390/jlpea12010010.

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