Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems
Author:
Affiliation:
1. Northeastern University,Dept. of Electrical and Computer Engineering,Boston,MA,USA
2. Lawrence Livermore National Laboratory,Livermore,CA,USA
Funder
National Science Foundation (NSF)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9774496/9774497/09774523.pdf?arnumber=9774523
Reference29 articles.
1. Mest: Accurate and fast memory-economic sparse training framework on the edge;yuan;Advances in neural information processing systems,2021
2. DMCP: Differentiable Markov Channel Pruning for Neural Networks
3. A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers
4. Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
5. Hardware realization of BSB recall function using memristor crossbar arrays
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1. FARe: Fault-Aware GNN Training on ReRAM-Based PIM Accelerators;2024 Design, Automation & Test in Europe Conference & Exhibition (DATE);2024-03-25
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3. Real-Time Diagnostic Technique for AI-Enabled System;IEEE Open Journal of Intelligent Transportation Systems;2024
4. TFix: Exploiting the Natural Redundancy of Ternary Neural Networks for Fault Tolerant In-Memory Vector Matrix Multiplication;2023 60th ACM/IEEE Design Automation Conference (DAC);2023-07-09
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