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.
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.
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.
The paper introduces OrbitALIF, a federated learning framework that performs cloud removal on low‑earth‑orbit satellites. It uses a compact 2.30 M‑parameter spiking neural network with adaptive gated fusion and spectral‑spatial hybrid attention modules, enabling both training and inference onboard. The approach achieves competitive cloud‑removal quality while consuming only 0.287 mJ per inference on neuromorphic hardware, a 72.3‑fold energy reduction compared to an equivalent ANN.
The paper introduces the Dormant Spiking Neuron (DSN), a state-driven mechanism that allows neurons in spiking neural networks to enter a dormant state when their membrane potential falls below a negative threshold. This dynamic gating prunes redundant computations at the instance level and is trained end‑to‑end via backpropagation through time with surrogate gradients. Experiments on static and neuromorphic datasets, including ImageNet, show that DSN reduces computational energy by 40% to over 70% and improves baseline SNN accuracy by 1.34%, outperforming state‑of‑the‑art static pruning methods.
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:2604. 08894v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs).
STATrack is a fully spiking neural network designed for UAV visual tracking using only RGB inputs, eliminating the need for costly event cameras. It introduces Adaptive Mutual Information Maximization (AMIM) to preserve fine-grained target information in deep spiking representations and a sample-difficulty-aware dynamic weighting strategy to adjust the mutual‑information constraint during training. Experiments on four UAV tracking benchmarks show that STATrack achieves state‑of‑the‑art performance while maintaining low theoretical energy consumption.
Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain largely inherited from terrestrial federated learning...
The paper introduces a heterogeneous federated learning approach using the FractalNet architecture tailored for satellite mega‑constellations. It formalizes contact‑window‑constrained, depth‑heterogeneous optimization and proposes a distributed path scheduler that assigns model depth based on satellite SWAP‑C constraints, predicted contacts, and training statistics. The framework includes periodic update pooling and a three‑tier agentic control plane, and is validated through a wildfire detection case study across LEO, MEO, and GEO/HEO shells, demonstrating improvements in convergence, communication efficiency, energy adaptation, and robustness.
SpikeMoE introduces a spike-based k‑WTA router that uses lateral inhibition and refractory periods to select the top‑K experts based on discrete spike counts, inspired by hippocampal CA1 competition. The framework combines spiking neural network dynamics with mixture‑of‑experts conditional computation and adds a two‑stage missing‑modality module for robust multimodal processing. Experiments on vision, language, and multimodal tasks show that SpikeMoE matches or surpasses ANN baselines while offering energy‑efficient performance.
arXiv:2606. 13901v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics.
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.