Hardening a Neural Network on FPGA through Selective Triplication and Training Optimization

The proposed approach identifies SEU sensitive flip-flops and optimizes the training phase of a neural network. With this information, selective triplication is applied to improve the reliability with limited resource overhead on an FPGA device.