# Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators

arXiv: https://arxiv.org/abs/2101.02860 · HTML: https://mlsanity.com/paper/2101.02860/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks](https://mlsanity.com/paper/1906.01017/) (2019)
- [Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions](https://mlsanity.com/paper/1803.05900/) (2018)
- [Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator](https://mlsanity.com/paper/1802.04657/) (2018)
- [ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations](https://mlsanity.com/paper/1912.00700/) (2019)
- [Approximate Random Dropout](https://mlsanity.com/paper/1805.08939/) (2018)
- [RAPIDNN: In-Memory Deep Neural Network Acceleration Framework](https://mlsanity.com/paper/1806.05794/) (2018)
- [High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators](https://mlsanity.com/paper/2006.03616/) (2020)
- [ApproxCS: Near-Sensor Approximate Compressed Sensing for IoT-Healthcare Systems](https://mlsanity.com/paper/1811.07330/) (2018)

## Built on by (most cited first, 6 of 6)

- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators](https://mlsanity.com/paper/2303.08226/) (2023)
- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?](https://mlsanity.com/paper/2112.01555/) (2021)
- [Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays](https://mlsanity.com/paper/2503.16583/) (2025)
- [EPSILON: Adaptive Fault Mitigation in Approximate Deep Neural Network using Statistical Signatures](https://mlsanity.com/paper/2504.20074/) (2025)

## Abstract (arXiv)

Systolic array-based deep neural network (DNN) accelerators have recently gained prominence for their low computational cost. However, their high energy consumption poses a bottleneck to their deployment in energy-constrained devices. To address this problem, approximate computing can be employed at the cost of some tolerable accuracy loss. However, such small accuracy variations may increase the sensitivity of DNNs towards undesired subtle disturbances, such as permanent faults. The impact of permanent faults in accurate DNNs has been thoroughly investigated in the literature. Conversely, the impact of permanent faults in approximate DNN accelerators (AxDNNs) is yet under-explored. The impact of such faults may vary with the fault bit positions, activation functions and approximation errors in AxDNN layers. Such dynamacity poses a considerable challenge to exploring the trade-off between their energy efficiency and fault resilience in AxDNNs. Towards this, we present an extensive layer-wise and bit-wise fault resilience and energy analysis of different AxDNNs, using the state-of-the-art Evoapprox8b signed multipliers. In particular, we vary the stuck-at-0, stuck-at-1 fault-bit positions, and activation functions to study their impact using the most widely used MNIST and Fashion-MNIST datasets. Our quantitative analysis shows that the permanent faults exacerbate the accuracy loss in AxDNNs when compared to the accurate DNN accelerators. For instance, a permanent fault in AxDNNs can lead up to 66% accuracy loss, whereas the same faulty bit can lead to only 9% accuracy loss in an accurate DNN accelerator. Our results demonstrate that the fault resilience in AxDNNs is orthogonal to the energy efficiency.