The paper presents a formal framework for applying spiking neural networks to satellite onboard computing, deriving closed‑form energy models based on network sparsity, firing rate, and fan‑out. It introduces three novel spike‑based algorithms—SKCF for attitude estimation, TCAD for health monitoring, and SAMC for adaptive communication—alongside analytical complexity bounds. A quantitative Energy‑Accuracy‑Latency surface, populated with silicon data from Loihi and SpiNNaker, demonstrates that neuromorphic solutions can reduce energy consumption by 8 to 47 times compared to GPU baselines while keeping inference accuracy within 2.1% for typical satellite workloads.
By Suresh Kumar TP, Vikas Agnihotri
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...
By Jingya Wang, Dehao Zhang, Shuai Wang, Malu Zhang, Yang Yang, Haizhou Li
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...
By Jingang Jin, Yue Ma, Zhenhang Zhang, Qinru Qiu
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
The paper introduces four new event‑based vision datasets created with the ANTShapes simulation tool, designed to support object classification research using spiking neural networks (SNNs). These datasets vary in difficulty and are benchmarked against established spiking datasets such as N‑MNIST, CIFAR10‑DVS, DVSGesture, and POKER‑DVS using a convolutional SNN. The work provides detailed, high‑quality datasets for future experiments and validates ANTShapes as a suitable tool for generating event‑based vision data.
By M. Middleton, H. Kayan, B. Sen Bhattacharya, T. Ali, E. Baikas, M. Vousden, C. Perera, O. Rhodes, E. Gheorghiu, M. A. Trefzer
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.
By Arooba Farhan, Afsah Syed, Muhammad Fahd Ibrahim Bhatti, Muhammad Areeb Khalid Kidwai, Muhammad Ahsan Salar Khan, Vincenzo Romano, Muhammad Ali Siddiqi
The paper introduces the Sparse-Activation-ReLU (SAR) layer, a single‑step neural operator that promotes activation sparsity without surrogate‑gradient training and is compatible with event‑based computing. In a trunk‑based NOMAD architecture, SAR improves the combined Latency‑Error‑Energy (LEE) metric by over fivefold compared to Variable Spiking Neuron (VSN) and Leaky Integrate‑and‑Fire (LIF) models. Additional techniques such as synthetic knowledge distillation, a ReLU‑based spiking loss, and graph‑neighbor thresholding further reduce LEE and L2 error on the Heat Exchanger dataset, advancing energy‑efficient virtual sensing for edge deployment.
By William Howes, Farid Ahmed, Syed Bahauddin Alam
Efficient video human pose estimation must balance localisation accuracy, motion quality, and energy cost. We present SpikePose, a lightweight video pose estimator that pairs a frame-reset, single-ste...
arXiv:2606. 10008v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework.
By Barry M. Dillon
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:2611.06594v1 Announce Type: new
Abstract: Efficient video human pose estimation must balance localisation accuracy, motion quality, and energy cost. We present SpikePose, a lightweight video po...
By Yunhao Huang, Kaushik Bhargav Sivangi, Tianyi Yu, Fani Deligianni
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