# PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators

arXiv: https://arxiv.org/abs/2603.08724 · HTML: https://mlsanity.com/paper/2603.08724/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification](https://mlsanity.com/paper/2205.07877/) (2022)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies](https://mlsanity.com/paper/1909.04697/) (2019)
- [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)
- [DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment](https://mlsanity.com/paper/2303.06931/) (2023)
- [Enhancing Fault Resilience of QNNs by Selective Neuron Splitting](https://mlsanity.com/paper/2306.09973/) (2023)
- [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)
- [CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA](https://mlsanity.com/paper/2004.04641/) (2020)
- [scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation](https://mlsanity.com/paper/2303.02495/) (2023)
- [Noise-Tolerance GPU-based Age Estimation Using ResNet-50](https://mlsanity.com/paper/2305.00848/) (2023)
- [AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators](https://mlsanity.com/paper/2403.02936/) (2024)
- [LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment](https://mlsanity.com/paper/2303.08915/) (2023)

## Built on by (most cited first, 1 of 1)

- [SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors](https://mlsanity.com/paper/2606.07620/) (2026)

## Abstract (arXiv)

This manuscript summarizes the work and showcases the impact of the doctoral thesis by introducing novel, cost-efficient methods for assessing and enhancing the reliability of DNN hardware accelerators. A comprehensive Systematic Literature Review (SLR) was conducted, categorizing existing reliability assessment techniques, identifying research gaps, and leading to the development of new analytical reliability assessment tools. Additionally, this work explores the interplay between reliability, quantization, and approximation, proposing methodologies that optimize the trade-offs between computational efficiency and fault tolerance. Furthermore, a real-time, zero-overhead reliability enhancement technique, AdAM, was developed, providing fault tolerance comparable to traditional redundancy methods while significantly reducing hardware costs. The impact of this research extends beyond academia, contributing to multiple funded projects, masters courses, industrial collaborations, and the development of new tools and methodologies for efficient and reliable DNN hardware accelerators.