Long Live TIME: Improving Lifetime and Security for NVM-Based Training-in-Memory Systems-Reference-Cited by-同舟云学术

Long Live TIME: Improving Lifetime and Security for NVM-Based Training-in-Memory Systems

Published:2020-12 Issue:12 Volume:39 Page:4707-4720
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Cai Yi^ORCID,Lin Yujun^ORCID,Xia Lixue,Chen Xiaoming^ORCID,Han Song^ORCID,Wang Yu^ORCID,Yang Huazhong^ORCID

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing National Research Center for Information Science and Technology

Beijing Innovation Center for Future Chips

Beijing Academy of Artificial Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

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1. Endurance-Aware Deep Neural Network Real-Time Scheduling on ReRAM Accelerators;2023 International Conference on Computational Science and Computational Intelligence (CSCI);2023-12-13

2. Digital Twin Based Fault-Tolerance Framework for RRAM Based Neural Computing Systems;2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS);2023-08-06