Investigating the effect of approximate multipliers on the resilience of a systolic array DNN accelerator-Reference-Cited by-同舟云学术

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Investigating the effect of approximate multipliers on the resilience of a systolic array DNN accelerator

Published:2023-10-03 Issue: Volume: Page:
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Container-title:2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)
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Author:

Pappalardo Salvatore¹,Piri Ali¹,Ruospo Annachiara²,O’Connor Ian¹,Deveautour Bastien¹,Sanchez Ernesto²,Bosio Alberto¹

Affiliation:

1. Univ Lyon, ECL, INSA Lyon, CNRS, UCBL, CPE Lyon, INL, UMR5270,Ecully,France,69130

2. Politecnico di Torino,Dip. di Automatica e Informatica,Torino,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10313343/10313345/10313535.pdf?arnumber=10313535

Reference25 articles.

1. Safety Critical Systems: Challenges and Directions;john c knight;Proceedings of the 24th International Conference on Software Engineering ICSE 2002,2002

2. A Survey on Deep Learning Resilience Assessment Methodologies

3. Selective Hardening of Critical Neurons in Deep Neural Networks

4. A Reliability Analysis of a Deep Neural Network

Cited by 1 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. Approximate Fault-Tolerant Neural Network Systems;2024 IEEE European Test Symposium (ETS);2024-05-20

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