  Deep Learning is able to solve a plethora of once impossible problems.
However, they are vulnerable to input adversarial attacks preventing them from
being autonomously deployed in critical applications. Several
algorithm-centered works have discussed methods to cause adversarial attacks
and improve adversarial robustness of a Deep Neural Network (DNN). In this
work, we elicit the advantages and vulnerabilities of hybrid 6T-8T memories to
improve the adversarial robustness and cause adversarial attacks on DNNs. We
show that bit-error noise in hybrid memories due to erroneous 6T-SRAM cells
have deterministic behaviour based on the hybrid memory configurations (V_DD,
8T-6T ratio). This controlled noise (surgical noise) can be strategically
introduced into specific DNN layers to improve the adversarial accuracy of
DNNs. At the same time, surgical noise can be carefully injected into the DNN
parameters stored in hybrid memory to cause adversarial attacks. To improve the
adversarial robustness of DNNs using surgical noise, we propose a methodology
to select appropriate DNN layers and their corresponding hybrid memory
configurations to introduce the required surgical noise. Using this, we achieve
2-8% higher adversarial accuracy without re-training against white-box attacks
like FGSM, than the baseline models (with no surgical noise introduced). To
demonstrate adversarial attacks using surgical noise, we design a novel,
white-box attack on DNN parameters stored in hybrid memory banks that causes
the DNN inference accuracy to drop by more than 60% with over 90% confidence
value. We support our claims with experiments, performed using benchmark
datasets-CIFAR10 and CIFAR100 on VGG19 and ResNet18 networks.
