arXiv Machine Learning June 10, 2026 By Barry M. Dillon

Spiking Neural Network inference on FPGAs with hls4ml

Read the original on arXiv Machine Learning →

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

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.

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 Machine Learning

Jul 10

FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs

arXiv:2607. 08427v1 Announce Type: cross Abstract: Achieving nanosecond-scale inference latency for deep neural networks (DNNs) has become a primary architectural concern for latency-critical applications.

By Jiawei Liang, Haotong Qin, Linfeng Du, Xingyu Liu, Shangkun Li, Hui Yu, Michele Magno, Xinyu Chen, Jiang Xu, Wei Zhang

efficiency

arXiv AI

Sep 1

ITP-STDP: A Hardware-Efficient Intrinsic-Timing Power-of-Two Synaptic Learning Engine for On-Chip SNNs

arXiv:2606.06159v2 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attentio...

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

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

Sep 1

Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition

arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificia...

By Valentin M. Meunier, Am\'elie Gruel, Pierre Lewden, Adrien F. Vincent, Sylvain Sa\"ighi

multimodal benchmarks

arXiv Machine Learning

Aug 12

Delays in Spiking Neural Networks: A State Space Model Approach

arXiv:2512. 01906v3 Announce Type: replace Abstract: Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing.

By Sanja Karilanova, Subhrakanti Dey, Ay\c{c}a \"Oz\c{c}elikkale

One email a morning, machine-written

Related stories

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

FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs

ITP-STDP: A Hardware-Efficient Intrinsic-Timing Power-of-Two Synaptic Learning Engine for On-Chip SNNs

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

Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition

Delays in Spiking Neural Networks: A State Space Model Approach