arXiv Machine Learning July 31, 2026 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Representation Capacity-Matched QNN-SNN Twin Construction for Rate-Encoded SNNs

arXiv:2409. 08290v5 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation.

By Zhanglu Yan, Zhenyu Bai, Kaiwen Tang, Weng-Fai Wong

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Jul 15

Burst Spiking Neural Networks

arXiv:2607. 11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs).

By Jiahong Zhang, Sijun Shen, Man Yao, Han Xu, Mingqiang Huang, Yonghong Tian, Bo Xu, Guoqi Li

efficiency benchmarks

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Aug 5

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning

arXiv:2608. 03324v1 Announce Type: new Abstract: Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy.

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efficiency benchmarks safety

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Aug 5

SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels

arXiv:2608. 02995v1 Announce Type: cross Abstract: Modern large language models (LLMs) exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens.

By Yongwan Jo, Jinyoung Park, Euihyun Lee, Dokyung Song

llms efficiency

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Jun 2

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

arXiv:2606. 01442v1 Announce Type: cross Abstract: Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment.

By Raj Patel, David Amebley, Taye Akinrele, Shaswata Mitra, Sayanton Dibbo, Shahram Rahimi

benchmarks

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Aug 11

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.

By Prasanna Date, Kevin Zhu, Shruti Kulkarni, Ashish Gautam, Chathika Gunaratne, Robert Patton, Tyler Nitzsche, Ian Mulet, Zachary Johnson-Scott, Addison Helms, Duncan Rowden, Simon Weston, Maryam Parsa, Catherine Schuman, Thomas Potok

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