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.
Under- standing the impact of precision quantization on the accuracy and energy of neural networks,
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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.