A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
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2 Pith papers cite this work. Polarity classification is still indexing.
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ImageHD delivers up to 40.4x speedup and 383x energy efficiency for on-device continual learning of visual representations by using hyperdimensional computing and bounded exemplar management on an FPGA.
citing papers explorer
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Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
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ImageHD: Energy-Efficient On-Device Continual Learning of Visual Representations via Hyperdimensional Computing
ImageHD delivers up to 40.4x speedup and 383x energy efficiency for on-device continual learning of visual representations by using hyperdimensional computing and bounded exemplar management on an FPGA.