Cost- and Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators
Author:
Publisher
IEEE
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http://xplorestaging.ieee.org/ielx7/9473901/9473226/09474226.pdf?arnumber=9474226
Cited by 8 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. Partial Sum Quantization for Reducing ADC Size in ReRAM-Based Neural Network Accelerators;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-12
2. CRIMP: C ompact & R eliable DNN Inference on I n- M emory P rocessing via Crossbar-Aligned Compression and Non-ideality Adaptation;ACM Transactions on Embedded Computing Systems;2023-09-09
3. Reconfigurable Mapping Algorithm based Stuck-At-Fault Mitigation in Neuromorphic Computing Systems;Proceedings of the Great Lakes Symposium on VLSI 2023;2023-06-05
4. Resistive Neural Hardware Accelerators;Proceedings of the IEEE;2023-05
5. Dynamic Task Remapping for Reliable CNN Training on ReRAM Crossbars;2023 Design, Automation & Test in Europe Conference & Exhibition (DATE);2023-04
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