The paper introduces two compact spiking neural networks for real‑time classification of Purkinje cell spikes on untethered headstages. ConvSNN is mapped to the Matrix Vector Processor of an EFM32PG28 microcontroller, achieving a CS F1 of 0.873 with 1,018 parameters and reducing latency by 62.3% while extending battery life to 33.73 hours. StreamSNN processes one waveform value per timestep on a dedicated SKY130 circuit, reaching a CS F1 of 0.837 with 527 parameters, occupying 0.173 mm² and consuming 0.46 µJ per event with 0.2 ms computation within an 8.3 ms end‑to‑end latency.
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.
arXiv:2610.17463v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient spatiotemporal pattern recognition at the edge, but efficient deployment and on-devic...
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: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: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: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: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:2604. 08894v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs).
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.
arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.
The paper evaluates a spiking version of the MoveNet architecture, called S-MoveNet-Lightning, for autonomous spacecraft pose estimation. Using the RAPTOR dataset, it compares the spiking model to the standard MoveNet-Lightning, finding that the spiking version requires on average 2.2 times fewer multiplication and accumulation operations on FPGA platforms with AMD Deep Learning Processor Units. The study highlights balanced advantages in computational resources and architectural settings for spiking U‑Net‑like architectures in embedded, event‑driven space applications.
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...