FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications, spanning from image classification to speech recognition. While providing excellent accuracy, they often have enormous compute and memory requirements. As a result of this, quantized neural networks (QNNs) are increasingly being adopted and deployed especially on embedded devices, thanks to …