Special Session: Approximation and Fault Resiliency of DNN Accelerators
Author:
Affiliation:
1. Tallinn University of Technology,Tallinn,Estonia
2. University of Naples Federico II,Naples,Italy
3. Ecole Centrale de Lyon,Lyon,France
4. Mälardalen University,Västerås,Sweden
5. Politecnico di Torino,Torino,Italy
Funder
European Social Fund
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10139918/10139926/10140043.pdf?arnumber=10140043
Reference47 articles.
1. DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
2. EvoApprox8b: Library of Approximate Adders and Multipliers for Circuit Design and Benchmarking of Approximation Methods
3. Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems
4. SHIELDeNN: Online Accelerated Framework for Fault-Tolerant Deep Neural Network Architectures
5. Pros and Cons of Fault Injection Approaches for the Reliability Assessment of Deep Neural Networks
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4. AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators;2024 IEEE European Test Symposium (ETS);2024-05-20
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