# QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks

arXiv: https://arxiv.org/abs/2004.11233 · HTML: https://mlsanity.com/paper/2004.11233/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [On the Robustness of Bayesian Neural Networks to Adversarial Attacks](https://mlsanity.com/paper/2207.06154/) (2022)
- [CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification](https://mlsanity.com/paper/2212.02770/) (2022)
- [2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency](https://mlsanity.com/paper/2109.05223/) (2021)
- [Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks](https://mlsanity.com/paper/2101.04354/) (2021)
- [Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks](https://mlsanity.com/paper/2105.04003/) (2021)
- [A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness](https://mlsanity.com/paper/2110.12308/) (2021)
- [Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks](https://mlsanity.com/paper/2304.12829/) (2023)
- [Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks](https://mlsanity.com/paper/2101.01543/) (2021)
- [ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs](https://mlsanity.com/paper/2410.24214/) (2024)
- [DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning](https://mlsanity.com/paper/2208.00498/) (2022)
- [Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks](https://mlsanity.com/paper/2503.08973/) (2025)

## Abstract (arXiv)

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.