Leveraging Noise and Aggressive Quantization of In-Memory Computing for Robust DNN Hardware Against Adversarial Input and Weight Attacks-Reference-Cited by-同舟云学术

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Leveraging Noise and Aggressive Quantization of In-Memory Computing for Robust DNN Hardware Against Adversarial Input and Weight Attacks

Published:2021-12-05 Issue: Volume: Page:
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Container-title:2021 58th ACM/IEEE Design Automation Conference (DAC)
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Author:

Cherupally Sai Kiran,Rakin Adnan Siraj,Yin Shihui,Seok Mingoo,Fan Deliang,Seo Jae-sun

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9585997/9586083/09586233.pdf?arnumber=9586233

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3. Adversarial Attacks on Large Language Model-Based System and Mitigating Strategies: A Case Study on ChatGPT;Security and Communication Networks;2023-06-10

4. A Survey of Bit-Flip Attacks on Deep Neural Network and Corresponding Defense Methods;Electronics;2023-02-08

5. A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks;2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS);2022-09-12

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