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Presentation

Pruning of Deep Neural Networks for Fault-Tolerant Memristor-based Accelerators
Time
Location
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
Manufacturing Test and Reliability
Topics
EDA
DescriptionCritical hardware faults (CFs) lead to misclassification of deep neural networks (DNNs) mapped to memristor crossbars. We present a machine learning technique to identify these CFs with over 98% accuracy and is 20x faster than a baseline using random fault injection. Next, we present a fault-tolerance technique that iteratively prunes a DNN by targeting weights mapped to CFs in the crossbars. Our results show that the proposed pruning technique eliminates up to 95\% of the CFs with negligible DNN accuracy loss, leading to a 99% savings in the hardware redundancy required for fault tolerance.