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Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach

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Cao, Linfeng 
Yang, Liujia 
Zhang, Ziqing 
Fang, Zhicheng 


jats:titleAbstract</jats:title>jats:pAnalog deep neural networks (DNNs) provide a promising solution, especially for deployment on resource-limited platforms, for example in mobile settings. However, the practicability of analog DNNs has been limited by their instability due to multi-factor reasons from manufacturing, thermal noise, etc. Here, we present a theoretically guaranteed noise injection approach to improve the robustness of analog DNNs without any hardware modifications or sacrifice of accuracy, which proves that within a certain range of parameter perturbations, the prediction results would not change. Experimental results demonstrate that our algorithmic framework can outperform state-of-the-art methods on tasks including image classification, object detection, and large-scale point cloud object detection in autonomous driving by a factor of 10 to 100. Together, our results may serve as a way to ensure the robustness of analog deep neural network systems, especially for safety-critical applications.</jats:p>


Acknowledgements: N.Y. acknowledges the funding from the National Science Foundation of China (Grant No. 62106139). Q.G. is grateful for the support from the Shanghai Artificial Intelligence Laboratory and the National Key R&D Program of China (Grant NO.2022ZD0160100). G.-Z.Y. acknowledges the funding from the Science and Technology Commission of Shanghai Municipality (Grant No. 20DZ2220400).


46 Information and Computing Sciences, 4611 Machine Learning, Machine Learning and Artificial Intelligence, Bioengineering

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Communications Engineering

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Springer Science and Business Media LLC
National Natural Science Foundation of China (National Science Foundation of China) (62106139)