NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
Abstract
Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer...
Paper Details
Title
NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
Published Date
May 16, 2020
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