SHIELDeNN: Online accelerated framework for fault-tolerant deep neural network architectures
Date of Original Version
We propose SHIELDeNN, an end-to-end inference accelerator frame-work that synergizes the mitigation approach and computational resources to realize a low-overhead error-resilient Neural Network (NN) overlay. We develop a rigorous fault assessment paradigm to delineate a ground-truth fault-skeleton map for revealing the most vulnerable parameters in NN. The error-susceptible parameters and resource constraints are given to a function to find superior design. The error-resiliency magnitude offered by SHIELDeNN can be adjusted based on the given boundaries. SHIELDeNN methodology improves the error-resiliency magnitude of cnvW1A1 by 17.19% and 96.15% for 100 MBUs that target weight and activation layers, respectively.
Proceedings - Design Automation Conference
Khoshavi, Navid, Arman Roohi, Connor Broyles, Saman Sargolzaei, Yu Bi, and David Z. Pan. "SHIELDeNN: Online accelerated framework for fault-tolerant deep neural network architectures." Proceedings - Design Automation Conference 2020-July, (2020). doi:10.1109/DAC18072.2020.9218697.