# Special Session: Approximation and Fault Resiliency of DNN Accelerators

arXiv: https://arxiv.org/abs/2306.04645 · HTML: https://mlsanity.com/paper/2306.04645/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators](https://mlsanity.com/paper/2303.08226/) (2023)
- [DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment](https://mlsanity.com/paper/2303.06931/) (2023)
- [When Neurons Fail](https://mlsanity.com/paper/1706.08884/) (2017)
- [Challenges of Reliability Assessment and Enhancement in Autonomous Systems](https://mlsanity.com/paper/1909.03040/) (2019)

## Built on by (most cited first, 5 of 5)

- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators](https://mlsanity.com/paper/2403.02946/) (2024)
- [AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators](https://mlsanity.com/paper/2403.02936/) (2024)
- [HAWX: A Hardware-Aware FrameWork for Fast and Scalable ApproXimation of DNNs](https://mlsanity.com/paper/2602.16336/) (2026)
- [PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators](https://mlsanity.com/paper/2603.08724/) (2026)

## Abstract (arXiv)

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of hardware architectures running DNNs has to be evaluated, usually through costly fault injection campaigns. This paper explores the approximation and fault resiliency of DNN accelerators. We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based simulation framework. Further, we propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks