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