Efficient Fault-Criticality Analysis for AI Accelerators using a Neural Twin∗-Reference-Cited by-同舟云学术

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Efficient Fault-Criticality Analysis for AI Accelerators using a Neural Twin∗

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Test Conference (ITC)
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Author:

Chaudhuri Arjun,Chen Ching-Yuan,Talukdar Jonti,Madala Siddarth,Dubey Abhishek Kumar,Chakrabarty Krishnendu

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9611037/9611298/09611337.pdf?arnumber=9611337

Cited by 12 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. ALPRI-FI: A Framework for Early Assessment of Hardware Fault Resiliency of DNN Accelerators;Electronics;2024-08-15

2. FKeras: A Sensitivity Analysis Tool for Edge Neural Networks;ACM Journal on Autonomous Transportation Systems;2024-07-23

3. In-Field Fault Detection Framework for Edge Accelerator Using Autoencoder;2024 IEEE 8th International Test Conference India (ITC India);2024-07-21

4. Design Exploration of Fault-Tolerant Deep Neural Networks Using Posit Number Representation System;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-07

5. Testing for Multiple Faults in Deep Neural Networks;IEEE Design & Test;2024-06

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