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This paper investigates error propagation in Deep Learning Neural Network (DNN) accelerators, focusing on the resilience of DNN systems against soft errors caused by high-energy particles. It highlights the inadequacy of traditional resilience methods and proposes two cost-effective protection techniques to mitigate Silent Data Corruptions (SDCs) in DNN applications, particularly in safety-critical environments like self-driving cars. The findings indicate that DNN systems

Author

Xpert TechGame

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EN