# Single-Event Upset Analysis of a Systolic Array based Deep Neural Network Accelerator

arXiv: https://arxiv.org/abs/2405.15381 · HTML: https://mlsanity.com/paper/2405.15381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2024.

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

- [Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications](https://mlsanity.com/paper/2610.03117/) (2026)

## Abstract (arXiv)

Deep Neural Network (DNN) accelerators are extensively used to improve the computational efficiency of DNNs, but are prone to faults through Single-Event Upsets (SEUs). In this work, we present an in-depth analysis of the impact of SEUs on a Systolic Array (SA) based DNN accelerator. A fault injection campaign is performed through a Register-Transfer Level (RTL) based simulation environment to improve the observability of each hardware block, including the SA itself as well as the post-processing pipeline. From this analysis, we present the sensitivity, independent of a DNN model architecture, for various flip-flop groups both in terms of fault propagation probability and fault magnitude. This allows us to draw detailed conclusions and determine optimal mitigation strategies.