Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
Author:
Affiliation:
1. Tallinn University of Technology,Tallinn,Estonia
2. University of Alberta,Edmonton,Canada
3. Shahid Bahonar University of Kerman,Kerman,Iran
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10528356/10528364/10528372.pdf?arnumber=10528372
Reference29 articles.
1. Dnn hardware reliability assessment and enhancement;Taheri
2. A Survey of Quantization Methods for Efficient Neural Network Inference
3. A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
4. DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
5. APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors
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1. AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators;2024 IEEE European Test Symposium (ETS);2024-05-20
2. Special Session: Reliability Assessment Recipes for DNN Accelerators;2024 IEEE 42nd VLSI Test Symposium (VTS);2024-04-22
3. Keynote: Cost-Efficient Reliability for Edge-AI Chips;2024 IEEE 25th Latin American Test Symposium (LATS);2024-04-09
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