# DAVIDSTUTZ

## Random and Adversarial Bit Error Training

### Abstract

Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, and random bit error training (RandBET) improves robustness against random bit errors in (quantized) DNN weights significantly. This leads to high energy savings from both low-voltage operation as well as low-precision quantization. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays. We also discuss why weight clipping alone is already a quite effective way to achieve robustness against bit errors. Moreover, we specifically discuss the involved trade-offs regarding accuracy, robustness and precision: Without losing more than 1% in accuracy compared to a normally trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even for 4-bit DNNs.

### Paper

The paper is available on ArXiv:

@article{Stutz2020ARXIV,
author    = {David Stutz and Nandhini Chandramoorthy and Matthias Hein and Bernt Schiele},
title     = {Bit Error Robustness for Energy-Efficient DNN Accelerators},
journal   = {CoRR},
volume    = {abs/2006.13977},
year      = {2020}
}


Coming soon!