Floating-point weight formats in embedded neural networks suffer near-total accuracy loss from a single electromagnetic fault injection, while 8-bit integer formats retain substantially higher accuracy on the same hardware.
Tiny machine learning: Progress and futures [feature],
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A review of CubeSat intrusion detection challenges identifies gaps in current methods and positions TinyML as a resource-efficient solution while outlining future research directions.
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The Weight of a Bit: EMFI Sensitivity Analysis of Embedded Deep Learning Models
Floating-point weight formats in embedded neural networks suffer near-total accuracy loss from a single electromagnetic fault injection, while 8-bit integer formats retain substantially higher accuracy on the same hardware.
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Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions
A review of CubeSat intrusion detection challenges identifies gaps in current methods and positions TinyML as a resource-efficient solution while outlining future research directions.