Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks

Abstract

Deep Neural Networks (DNNs) have been shown to be prone to adversarial attacks. Memristive crossbars, being able to perform Matrix-Vector-Multiplications (MVMs) efficiently, are used to realize DNNs on hardware. However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to computational accuracy losses in DNNs. Several software-based defenses have been proposed to make DNNs adversarially robust. However, no previous work has demonstrated the advantage conferred by the crossbar non-idealities in unleashing adversarial robustness. We show that the intrinsic hardware non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization. We evaluate the adversarial resilience of state-of-the-art DNNs (VGG8 & VGG16 networks) using benchmark datasets (CIFAR-10, CIFAR-100 & Tiny Imagenet) across various crossbar sizes. We find that crossbar non-idealities unleash significantly greater adversarial robustness (>10-20%) in crossbar-mapped DNNs than baseline software DNNs. We further assess the performance of our approach with other state-of-the-art efficiency-driven adversarial defenses and find that our approach performs significantly well in terms of reducing adversarial loss.

7 pages, 10 figures, 3 tables

References in corpus (5)

• Explaining and Harnessing Adversarial Examples
• On Evaluating Adversarial Robustness
• A Survey of Neuromorphic Computing and Neural Networks in Hardware
• Discretization based Solutions for Secure Machine Learning against Adversarial Attacks
• X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks

Cited by in corpus (5)

• Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
• Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks
• Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks
• Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks
• DetectX -- Adversarial Input Detection using Current Signatures in Memristive XBar Arrays