  Deep neural network (DNN) accelerators received considerable attention in
recent years due to the potential to save energy compared to mainstream
hardware. Low-voltage operation of DNN accelerators allows to further reduce
energy consumption, however, causes bit-level failures in the memory storing
the quantized weights. Furthermore, DNN accelerators are vulnerable to
adversarial attacks on voltage controllers or individual bits. In this paper,
we show that a combination of robust fixed-point quantization, weight clipping,
as well as random bit error training (RandBET) or adversarial bit error
training (AdvBET) improves robustness against random or adversarial bit errors
in quantized DNN weights significantly. This leads not only to high energy
savings for low-voltage operation as well as low-precision quantization, but
also improves security of DNN accelerators. In contrast to related work, our
approach generalizes across operating voltages and accelerators and does not
require hardware changes. Moreover, we present a novel adversarial bit error
attack and are able to obtain robustness against both targeted and untargeted
bit-level attacks. Without losing more than 0.8%/2% in test accuracy, we can
reduce energy consumption on CIFAR10 by 20%/30% for 8/4-bit quantization.
Allowing up to 320 adversarial bit errors, we reduce test error from above 90%
(chance level) to 26.22%.
