arXiv AI June 24, 2026 By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.

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

arXiv AI

Jul 29

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

efficiency

arXiv AI

Aug 7

Ge$^\text{2}$mS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer

arXiv:2604. 08894v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs).

By Zecheng Hao, Shenghao Xie, Kang Chen, Wenxuan Liu, Zhaofei Yu, Tiejun Huang

llms benchmarks

arXiv AI

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

arXiv AI

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

benchmarks

arXiv AI

Jun 11

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

arXiv:2606. 12287v1 Announce Type: cross Abstract: The Transformer architecture is widely regarded as the most powerful tool for natural language processing, but due to a high number of complex operations, it inherently faces the issue of high energy consumption.

By Claas Beger, Florian Walter, Alois Knoll

llms rag computer-vision nlp

arXiv Machine Learning

Jun 16

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

arXiv:2506. 20015v2 Announce Type: replace Abstract: Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators, particularly for real-time processing of time-series data.

By Dengyu Wu, Jiechen Chen, H. Vincent Poor, Bipin Rajendran, Osvaldo Simeone

efficiency

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

Ge$^\text{2}$mS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer

Burst Spiking Neural Networks

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

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons