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arXiv Machine Learning By Spyridon Raptis, Haralampos-G. Stratigopoulos

Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks

Read the original on arXiv Machine Learning →

arXiv:2607. 27990v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) communicate through sparse binary spike events rather than dense activations, enabling energy-efficient inference on neuromorphic hardware and motivating their use in always-on, battery-powered edge systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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