Learning to Predict IR Drop with Effective Training for ReRAM-based Neural Network Hardware-Reference-Cited by-同舟云学术

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Learning to Predict IR Drop with Effective Training for ReRAM-based Neural Network Hardware

Published:2020-07 Issue: Volume: Page:
ISSN:
Container-title:2020 57th ACM/IEEE Design Automation Conference (DAC)
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Author:

Lee Sugil,Jung Giju,Fouda Mohammed E.,Lee Jongeun,Eltawil Ahmed,Kurdahi Fadi

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9211868/9218488/09218735.pdf?arnumber=9218735

Cited by 30 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. Compensation architecture design utilizing residual resource to mitigate impacts of nonidealities in RRAM-based computing-in-memory chips;Microelectronics Journal;2024-07

2. Vertical AND-Type Flash TFT Array Capable of Accurate Vector-Matrix Multiplication Operations for Hardware Neural Networks;IEEE Electron Device Letters;2024-07

3. Compensation Architecture to Alleviate Noise Effects in RRAM-based Computing-in-memory Chips with Residual Resource;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

4. Reliability analysis and mitigation for analog computation-in-memory: from technology to application;2024 IEEE 42nd VLSI Test Symposium (VTS);2024-04-22

5. PyAIM: Pynq-Based Scalable Analog In-Memory Computing Prototyping Platform;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

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