<ul><li>Fault tolerance in Deep Neural Networks (DNNs) deployed on resource-constrained systems presents unique challenges for high-accuracy applications with strict timing requirements.</li><li>The novel protection approach NAPER employs ensemble learning and heterogeneous model redundancy to achieve higher accuracy than traditional redundancy methods.</li><li>NAPER provides an efficient fault detection mechanism and a real-time scheduler to prioritize meeting deadlines and ensure uninterrupted operation during fault recovery.</li><li>Comparative evaluations show that NAPER offers 40% faster inference, 4.2% higher accuracy than TMR-based strategies, and effectively balances accuracy, reliability, and timeliness in real-time DNN applications.</li></ul>

NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks

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