arXiv Machine Learning

DBNN: Neural Spike Classification Using a Deep Binarized Neural Network

arXiv:2607. 05590v1 Announce Type: cross Abstract: Implantable brain-computer interfaces require on-node spike sorting to reduce telemetry bandwidth and power while maintaining reliable neural decoding.

arXiv AI

Jun 24

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.

By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua

llms benchmarks

arXiv AI

Jun 6

ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training

arXiv:2606. 06159v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across a wide range of applications.

By Haihang Xia, Xinyu Zhao, Xuecheng Wang, John Goodenough, Charith Abhayaratne, Panagiotis A. Panagiotou, Chunyi Song, Tiantai Deng

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

Jul 28

The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

arXiv:2607. 24396v1 Announce Type: cross Abstract: In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications.

By Stefan Scholze, Johannes Partzsch, Sebastian H\"oppner, Florian Kelber, Andreas Dixius, Marco Stolba, Sirine Arfa, Marc Berthel, Georg Ellguth, Jim Garside, Hector A. Gonzalez, Stephan Hartmann, Thomas Kiel-Hocker, Dongwei Hu, Matthias Jobst, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Gengting Liu, Matthias Lohrmann, Mantas Mikaitis, Felix Neum\"arker, Amirhossein Rostami, Stefan Schiefer, Tilo Schubert, Delong Shang, Bernhard Vogginger, Yexin Yan, Steve Furber, Christian Mayr

arXiv AI

Jun 12

Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer

arXiv:2606. 13016v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.

By Zhanglu Yan, Jiayi Mao, Kaiwen Tang, Fanfan Li, Gang Pan, Tao Luo, Bowen Zhu, Qianhui Liu, Weng-Fai Wong

llms efficiency

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

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

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

Jul 21

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

arXiv:2607. 18149v1 Announce Type: cross Abstract: Real-time EEG classification on edge devices is bottlenecked by the floating-point arithmetic of conventional neural networks.

By Shyamal Y. Dharia, Stephen D. Smith, Camilo E. Valderrama

efficiency

arXiv Machine Learning

Jul 17

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.

By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara

arXiv Machine Learning

Jun 10

Spiking Neural Network inference on FPGAs with hls4ml

arXiv:2606. 10008v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework.

By Barry M. Dillon

arXiv AI

Jul 21

Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks

arXiv:2607. 16296v1 Announce Type: cross Abstract: Continuous EEG monitoring for epilepsy is constrained by the limited power and memory budgets of wearable and implantable devices.

By Kartikey Ahlawat

efficiency

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