Enhancing neural network robustness: Laser fault injection resistance in 55-nm SRAM for space applications  

作　　者：Qing Liu Haomiao Cheng Xiang Yao Zhengxuan Zhang Zhiyuan Hu Dawei Bi 刘清;程浩淼;姚骧;张正选;胡志远;毕大炜

机构地区：[1]State Key Laboratory of Materials for Integrated Circuits,Shanghai Institute of Microsystem and Information Technology,Shanghai 200050,China [2]University of the Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Chinese Physics B》2025年第4期478-484,共7页中国物理B(英文版)

摘　　要：The integration of artificial intelligence(AI)with satellite technology is ushering in a new era of space exploration,with small satellites playing a pivotal role in advancing this field.However,the deployment of machine learning(ML)models in space faces distinct challenges,such as single event upsets(SEUs),which are triggered by space radiation and can corrupt the outputs of neural networks.To defend against this threat,we investigate laser-based fault injection techniques on 55-nm SRAM cells,aiming to explore the impact of SEUs on neural network performance.In this paper,we propose a novel solution in the form of Bin-DNCNN,a binary neural network(BNN)-based model that significantly enhances robustness to radiation-induced faults.We conduct experiments to evaluate the denoising effectiveness of different neural network architectures,comparing their resilience to weight errors before and after fault injections.Our experimental results demonstrate that binary neural networks(BNNs)exhibit superior robustness to weight errors compared to traditional deep neural networks(DNNs),making them a promising candidate for spaceborne AI applications.

关 键 词：single event effects convolutional neural network fault injection SRAM 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]