In-Place Zero-Space Memory Protection for CNN

NeurIPS 2019 Hui GuanLin NingZhen LinXipeng ShenHuiyang ZhouSeung-Hwan Lim

Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults. Traditional methods such as error correction codes (ECC) and Triple Modular Redundancy (TMR) are CNN-oblivious and incur substantial memory overhead and energy cost... (read more)

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