Enhancing Fault Resilience of QNNs by Selective Neuron Splitting

Author:

Ahmadilivani Mohammad Hasan1,Taheri Mahdi1,Raik Jaan1,Daneshtalab Masoud1,Jenihhin Maksim1

Affiliation:

1. Tallinn University of Technology,Tallinn,Estonia

Publisher

IEEE

Reference16 articles.

1. Deepvigor: Vulnerability value ranges and factors for dnns reliability assessment;ahmadilivani;28th IEEE European Test Symposium,2023

2. SalvageDNN: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping

3. Tensorflow lite micro: Embedded machine learning for tinyml systems;david;Machine Learning and Systems,2021

4. Just say zero

5. On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs

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1. Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning;2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS);2024-07-03

2. MRFI: An Open-Source Multiresolution Fault Injection Framework for Neural Network Processing;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-07

3. Special Session: Reliability Assessment Recipes for DNN Accelerators;2024 IEEE 42nd VLSI Test Symposium (VTS);2024-04-22

4. Keynote: Cost-Efficient Reliability for Edge-AI Chips;2024 IEEE 25th Latin American Test Symposium (LATS);2024-04-09

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