  Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial
attacks, wherein, a model gets fooled by applying slight perturbations on the
input. With the advent of Internet-of-Things and the necessity to enable
intelligence in embedded devices, low-power and secure hardware implementation
of DNNs is vital. In this paper, we investigate the use of quantization to
potentially resist adversarial attacks. Several recent studies have reported
remarkable results in reducing the energy requirement of a DNN through
quantization. However, no prior work has considered the relationship between
adversarial sensitivity of a DNN and its effect on quantization. We propose
QUANOS- a framework that performs layer-specific hybrid quantization based on
Adversarial Noise Sensitivity (ANS). We identify a novel noise stability metric
(ANS) for DNNs, i.e., the sensitivity of each layer's computation to
adversarial noise. ANS allows for a principled way of determining optimal
bit-width per layer that incurs adversarial robustness as well as
energy-efficiency with minimal loss in accuracy. Essentially, QUANOS assigns
layer significance based on its contribution to adversarial perturbation and
accordingly scales the precision of the layers. A key advantage of QUANOS is
that it does not rely on a pre-trained model and can be applied in the initial
stages of training. We evaluate the benefits of QUANOS on precision scalable
Multiply and Accumulate (MAC) hardware architectures with data gating and
subword parallelism capabilities. Our experiments on CIFAR10, CIFAR100 datasets
show that QUANOS outperforms homogenously quantized 8-bit precision baseline in
terms of adversarial robustness (3%-4% higher) while yielding improved
compression (>5x) and energy savings (>2x) at iso-accuracy.
