Reliable Memristor-based Neuromorphic Design Using Variation- and Defect-Aware Training

Author:

Gaol Di,Zhang Grace Li,Yin Xunzhao,Li Bing,Schlichtmann Ulf,Zhuo Cheng

Funder

NSFC

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fault Impact Map for Memristive Crossbar Neural Networks;2024 13th International Conference on Modern Circuits and Systems Technologies (MOCAST);2024-06-26

2. Analysis of Memristor Neural Networks for fault Tolerant Computing;2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS);2024-04-18

3. Advances in Physical Unclonable Functions Based on New Technologies: A Comprehensive Review;Mathematics;2023-12-25

4. On the Reliability of RRAM-Based Neural Networks;2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC);2023-10-16

5. Three Challenges in ReRAM-Based Process-In-Memory for Neural Network;2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS);2023-06-11

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