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.
An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification
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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).
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.
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.
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...
arXiv Computation and Language
Oct 8
SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
arXiv:2610.09710v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of...
arXiv Machine Learning
Oct 9
Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks
Local Timescale Gating (LT‑Gate) is a neuron model that tracks fast and slow information in parallel and uses a learned gate to adjust their influence, enabling spiking neurons to preserve long‑term context while responding quickly. The approach includes a variance‑tracking regularization inspired by biological homeostasis and operates with local updates, making it compatible with neuromorphic hardware such as Intel’s Loihi chip. Empirical results show LT‑Gate improves accuracy and retention on sequential learning tasks, achieving about 51 % final accuracy versus 46 % for a recent Hebbian baseline and outperforming prior SNN methods.
arXiv AI
Sep 10
Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies
arXiv:2605.30361v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because th...
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.
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.
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.
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.