A Resilience Framework for Synapse Weight Errors and Firing Threshold Perturbations in RRAM Spiking Neural Networks

Author:

Saha Anurup1,Amarnath Chandramouli1,Chatterjee Abhijit1

Affiliation:

1. Georgia Institute of Technology,School of Electrical and Computer Engineering,Atlanta,GA,30332

Funder

Georgia Institute of Technology

Publisher

IEEE

Reference13 articles.

1. Training spiking neural networks using lessons from deep learning;eshraghian;arXiv preprint arXiv 2109 12894,2021

2. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms;xiao;ArXiv 1708 07747,2017

3. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

4. Gradient-based learning applied to document recognition

5. Spiker: an FPGA-optimized Hardware accelerator for Spiking Neural Networks

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1. Study of Soft Errors in Spiking Neural Network Hardware;International Journal of High Speed Electronics and Systems;2024-07-31

2. Post-Manufacture Criticality-Aware Gain Tuning of Timing Encoded Spiking Neural Networks for Yield Recovery;2024 IEEE European Test Symposium (ETS);2024-05-20

3. Securing On-Chip Learning: Navigating Vulnerabilities and Potential Safeguards in Spiking Neural Network Architectures;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

4. Signature Driven Post-Manufacture Testing and Tuning of RRAM Spiking Neural Networks for Yield Recovery;2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC);2024-01-22

5. Testing and Reliability of Spiking Neural Networks: A Review of the State-of-the-Art;2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT);2023-10-03

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