# DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

arXiv: https://arxiv.org/abs/2303.08226 · HTML: https://mlsanity.com/paper/2303.08226/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks](https://mlsanity.com/paper/1906.01017/) (2019)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [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)
- [Accelerating CNN inference on FPGAs: A Survey](https://mlsanity.com/paper/1806.01683/) (2018)
- [DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment](https://mlsanity.com/paper/2303.06931/) (2023)
- [Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators](https://mlsanity.com/paper/2101.02860/) (2021)
- [CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA](https://mlsanity.com/paper/2004.04641/) (2020)
- [Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead](https://mlsanity.com/paper/2205.14420/) (2022)
- [High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators](https://mlsanity.com/paper/2006.03616/) (2020)

## Built on by (most cited first, 11 of 11)

- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors](https://mlsanity.com/paper/2305.19733/) (2023)
- [Special Session: Approximation and Fault Resiliency of DNN Accelerators](https://mlsanity.com/paper/2306.04645/) (2023)
- [SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators](https://mlsanity.com/paper/2403.02946/) (2024)
- [Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques](https://mlsanity.com/paper/2309.16733/) (2023)
- [Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications](https://mlsanity.com/paper/2307.11128/) (2023)
- [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)
- [REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration](https://mlsanity.com/paper/2609.17555/) (2026)
- [BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection](https://mlsanity.com/paper/2609.17562/) (2026)

## Abstract (arXiv)

While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. It raises the necessity of improving the reliability of DNN accelerators yet reducing the computational burden on the hardware platforms, i.e. reducing the energy consumption and execution time as well as increasing the efficiency of DNN accelerators. Therefore, the trade-off between hardware performance, i.e. area, power and delay, and the reliability of the DNN accelerator implementation becomes critical and requires tools for analysis. In this paper, we propose a framework DeepAxe for design space exploration for FPGA-based implementation of DNNs by considering the trilateral impact of applying functional approximation on accuracy, reliability and hardware performance. The framework enables selective approximation of reliability-critical DNNs, providing a set of Pareto-optimal DNN implementation design space points for the target resource utilization requirements. The design flow starts with a pre-trained network in Keras, uses an innovative high-level synthesis environment DeepHLS and results in a set of Pareto-optimal design space points as a guide for the designer. The framework is demonstrated in a case study of custom and state-of-the-art DNNs and datasets.