Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks-Reference-Cited by-同舟云学术

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Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks

Published:2021-02-01 Issue: Volume: Page:
ISSN:
Container-title:2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Bhattacharjee Abhiroop,Moitra Abhishek,Panda Priyadarshini

Funder

National Science Foundation, the Amazon Research Award, and the Technology Innovation Institute, Abu Dhabi

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9473901/9473226/09474001.pdf?arnumber=9474001

Cited by 9 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. HyDe: A Hybrid PCM/FeFET/SRAM Device-Search for Optimizing Area and Energy-Efficiencies in Analog IMC Platforms;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-12

2. SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-11

3. DINAR: Enabling Distribution Agnostic Noise Injection in Machine Learning Hardware;Proceedings of the 12th International Workshop on Hardware and Architectural Support for Security and Privacy;2023-10-29

4. Robustness for Embedded Machine Learning Using In-Memory Computing;Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing;2023-10-07

5. XploreNAS : Explore Adversarially Robust and Hardware-efficient Neural Architectures for Non-ideal Xbars;ACM Transactions on Embedded Computing Systems;2023-07-24

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