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

Key points

• The CubeSat carries an off-the-shelf low-power Ferroelectric RAM (FRAM) chip and runs repeated read/write tests according to a predefined pattern.1
• We compare every memory snapshot received from the satellite with a reference bitmap to identify bit errors.1
• We define a generative deep learning technique to automatically learn these spatial patterns and to synthetically generate additional error patterns mimicking the observed ones.1
• 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.1

Sources (1)

• [1]Injection of Space Radiation Errors on Low-power NVM using Generative Deep Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:52 PM

  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.

Extractive summary: sentences quoted from the sources.