Newest papers

Injection of Space Radiation Errors on Low-power NVM using Generative Deep Learning

Eleonora Giudici, Antonio Miele, Luca Mottola · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26724 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们开发了非波动性内存(NVM)设备的错误模型,该模型代表在低地球轨道(LEO)CubeSat任务中经历的物理错误的效果。CubeSat携带低功率的Ferroelectric RAM(FRAM)芯片,并根据预定义模式进行重复的读写测试。我们将从卫星获得的每个内存快照与参考比特图进行比较,以识别比特错误。我们在内存快照中视觉上分析错误的值分布,我们了解它们经常根据常规空间模式分布。我们定义了一种生成深度学习技术,以自动学习这些空间模式,并合成分布额外的错误模式来模仿所观察到的错误。该模型能够对一般应用实验的内存错误进行比较

Machine translation (Meta M2M100); the English original follows.

Abstract

We develop an error model for a Non-Volatile Memory (NVM) device that represents the effects of physical faults experienced in a CubeSat mission in Low Earth Orbit (LEO). The CubeSat carries an off-the-shelf low-power Ferroelectric RAM (FRAM) chip and runs repeated read/write tests according to a predefined pattern. We compare every memory snapshot received from the satellite with a reference bitmap to identify bit errors. We visually analyze erroneous values distributions in the memory snapshots and we understand that they are frequently distributed according to regular spatial patterns. We define a generative deep learning technique to automatically learn these spatial patterns and to synthetically generate additional error patterns mimicking the observed ones. The model is capable at producing memory errors for general application-level experimentation. Our preliminary validation indicates that when comparing the model output with the data we collect, 0.92 of the generated bit flips correspond to bit flips in the memory snapshots and the model captures 0.91 of the observed bit flips. A comparison of the spatial distribution of bit flips in 10,000 generated error patterns also provides a Wasserstein distance of 0.073, indicating that the two distributions differ by roughly 7% of the maximum possible displacement of their probability masses.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Artificial intelligence, Deep learning, Bitmap, Algorithm, CubeSat

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange