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CLI tools, utilities, converters, workflow automation, and productivity helpers
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- Spiking Polar Trajectory GeneratorSpiking Neural Network (SNN) architecture for generating polar trajectories on neuromorphic hardware, using a winner-take-all (WTA) core with accessory populations that induce controlled transitions in neural activity. Interpretable at the level of system dynamics, energy-efficient, and directly deployable on neuromorphic substrates for size/weight/power-constrained control. Applicable to neuromorphic control, trajectory generation, WTA dynamics, polar-coordinate motor control, robotic naviga...Votes: 0GitHub stars: 3
- Spiking Neural Network TrainingTraining methodologies for energy-efficient spiking neural networks (SNNs). Covers surrogate gradient methods, spike-timing-dependent plasticity (STDP), and neuromorphic implementation. Activation: SNN, spiking neural network, surrogate gradient, STDP, neuromorphic.Votes: 0GitHub stars: 3
- Spiking Approximations Of The Maxpooling Operation In Deep Snns**arXiv ID:** 2205.07076 **Authors:** Ramashish Gaurav, Bryan Tripp, Apurva Narayan **Published:** 2022-05-14T14:47:10Z **Abstract:** Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep SNNs, with Artificial Neural Network (ANN)-to-SNN conversion being highly successful. MaxPooling layers in Convolutional Neural Networks (CNNs) are an integral component to downsampl...Votes: 0GitHub stars: 3
- Spikebased Neuromorphic Computing For Nextgeneration Computer Vision**arXiv ID:** 2310.09692 **Authors:** Md Sakib Hasan, Catherine D. Schuman, Zhongyang Zhang, Tauhidur Rahman, Garrett S. Rose **Published:** 2023-10-15T01:05:35Z **Abstract:** Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through i...Votes: 0GitHub stars: 3
- Snn Reconstruction Autapse基于论文 'Reconstructing Spiking Neural Networks Using a Single Neuron with Autapses' 的神经科学研究方法论。arXiv:2603.24692v1Votes: 0GitHub stars: 3
- Simple And Complex Spiking Neurons Perspectives And Analysis In A Simple Stdp Scenario**arXiv ID:** 2207.04881 **Authors:** Davide Liberato Manna, Alex Vicente Sola, Paul Kirkland, Trevor Bihl, Gaetano Di Caterina **Published:** 2022-06-28T10:01:51Z **Abstract:** Spiking neural networks (SNNs) are largely inspired by biology and neuroscience and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. The integrate-and-fire (I&F) mode...Votes: 0GitHub stars: 3
- Silif Dbs Neuromorphic ControllerNeuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease - CMOS-implemented SiLIF-DBS controller achieving 5.85%/uW beta suppression efficiency with 75% power reduction vs open-loopVotes: 0GitHub stars: 3
- Shiftlif Efficient Multilevel Spiking Neurons With Poweroftwo Quantization**arXiv ID:** 2605.01866 **Authors:** Kaiwen Tang, Di Yu, Jiaqi Zheng, Changze Lv, Qianhui Liu, Zhanglu Yan, Weng-Fai Wong **Published:** 2026-05-03T13:20:53Z **Abstract:** Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (LIF) neurons communicate only through binary spikes, which severely limit representational capacity. Existing multi-level spiking neurons improve...Votes: 0GitHub stars: 3
- Scalable Snn Gpu ClustersScalable SNN simulation framework using GPU cluster parallelization. Addresses computational bottlenecks in large-scale spiking neural network simulation through distributed GPU computing, optimized event-driven architectures, and efficient spike communication protocols. Activation: scalable snn, gpu cluster spiking simulation, distributed snn computing, large scale neural simulation, parallel snn, gpu spiking network, distributed neural simulationVotes: 0GitHub stars: 3
- Scalable Eventbyevent Processing Of Neuromorphic Sensory Signals With Deep Statespace Models**arXiv ID:** 2404.18508 **Authors:** Mark Schöne, Neeraj Mohan Sushma, Jingyue Zhuge, Christian Mayr, Anand Subramoney, David Kappel **Published:** 2024-04-29T08:50:27Z **Abstract:** Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other valuable properties, such as a high dynamic range, are suppressed when the data is converted to a frame-based format. However, most cu...Votes: 0GitHub stars: 3
- Numa Balancing Snn PerformanceMethodology for optimizing spiking neural network simulation performance by managing NUMA balancing settings on HPC systems.Votes: 0GitHub stars: 3
- Neuromorphic Spiking Ring AttractorNeuromorphic Spiking Ring Attractor for proprioceptive joint-state estimation. Implements neural clusters with local recurrent excitation and global inhibition to encode joint angles on neuromorphic processors like DYNAP-SE2. Activation: spiking ring attractor, proprioceptive encoding, neuromorphic joint estimation, SRA network.Votes: 0GitHub stars: 3
- Neuromorphic Dreaming A Pathway To Efficient Learning In Artificial Agents**arXiv ID:** 2405.15616 **Authors:** Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone, Giacomo Indiveri **Published:** 2024-05-24T15:03:56Z **Abstract:** Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural netw...Votes: 0GitHub stars: 3
- Modalitydependent Memory Mechanisms In Crossmodal Neuromorphic Computing**arXiv ID:** 2512.18575 **Authors:** Effiong Blessing, Chiung-Yi Tseng, Somshubhra Roy, Junaid Rehman, Isaac Nkrumah **Published:** 2025-12-21T03:18:42Z **Abstract:** Memory-augmented spiking neural networks (SNNs) promise energy-efficient neuromorphic computing, yet their generalization across sensory modalities remains unexplored. We present the first comprehensive cross-modal ablation study of memory mechanisms in SNNs, evaluating Hopfield networks, Hierarchical Gated Recurrent Networks (...Votes: 0GitHub stars: 3
- Memristor Preprocessing Reservoir Computing忆阻器预处理储备池计算图像分类方法论。利用忆阻器交叉阵列作为可重构非线性预处理层，结合单层感知机实现高效图像分类。MNIST准确率95.89%，20%器件变异下保持94.2%。忆阻器I-V非线性和记忆效应天然适用于特征变换。Votes: 0GitHub stars: 3
- Integer State Dynamics Quantized Spiking NeuralSpiking neural networks (SNNs) support energy-efficient machine intelligence because event-driven computation and sparse activity map naturally to low-power digital hardware. In pr... Activation: quantized, integer-state, SNN, hardware, lattice field theory, statistical mechanicsVotes: 0GitHub stars: 3
- H2learn Snn AcceleratorHigh-efficiency hardware accelerator for BPTT-based Spiking Neural Network training. Design LUT-based processing elements, dual-sparsity-aware backward engine, and pipeline optimization. Achieve 7.38x area saving, 10.20x speedup vs GPU.Votes: 0GitHub stars: 3
- Geometryaware Spiking Graph Neural Network**arXiv ID:** 2508.06793 **Authors:** Bowen Zhang, Genan Dai, Hu Huang, Long Lan **Published:** 2025-08-09T02:52:38Z **Abstract:** Graph Neural Networks (GNNs) have demonstrated impressive capabilities in modeling graph-structured data, while Spiking Neural Networks (SNNs) offer high energy efficiency through sparse, event-driven computation. However, existing spiking GNNs predominantly operate in Euclidean space and rely on fixed geometric assumptions, limiting their capacity to model comple...Votes: 0GitHub stars: 3
- Explore Activation Sparsity In Recurrent Llms For Energyefficient Neuromorphic Computing**arXiv ID:** 2501.16337 **Authors:** Ivan Knunyants, Maryam Tavakol, Manolis Sifalakis, Yingfu Xu, Amirreza Yousefzadeh, Guangzhi Tang **Published:** 2025-01-09T19:13:03Z **Abstract:** The recent rise of Large Language Models (LLMs) has revolutionized the deep learning field. However, the desire to deploy LLMs on edge devices introduces energy efficiency and latency challenges. Recurrent LLM (R-LLM) architectures have proven effective in mitigating the quadratic complexity of self-attention,...Votes: 0GitHub stars: 3
- Eventbased Shape From Polarization With Spiking Neural Networks**arXiv ID:** 2312.16071 **Authors:** Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos, Oliver Cossairt **Published:** 2023-12-26T14:43:26Z **Abstract:** Recent advances in event-based shape determination from polarization offer a transformative approach that tackles the trade-off between speed and accuracy in capturing surface geometries. In this paper, we investigate event-based shape from polarization using Spiking Neural Networks (SNNs), introducing the Single-Timestep and ...Votes: 0GitHub stars: 3
- Even Faster Snn Simulation With Lazyeventdriven Plasticity And Shared Atomics**arXiv ID:** 2107.04092 **Authors:** Dennis Bautembach, Iason Oikonomidis, Antonis Argyros **Published:** 2021-07-08T20:13:54Z **Abstract:** We present two novel optimizations that accelerate clock-based spiking neural network (SNN) simulators. The first one targets spike timing dependent plasticity (STDP). It combines lazy- with event-driven plasticity and efficiently facilitates the computation of pre- and post-synaptic spikes using bitfields and integer intrinsics. It offers higher bandwi...Votes: 0GitHub stars: 3
- Enforcesnn Enabling Resilient And Energyefficient Spiking Neural Network Inference Considering Approximate Drams For Embedded Systems**arXiv ID:** 2304.04039 **Authors:** Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique **Published:** 2023-04-08T15:15:11Z **Abstract:** Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under unsupervised settings and low operational power/energy due to their bio-plausible computations. Previous studies identified that DRAM-based off-chip memory accesses dominate the energy consumption of SNN processing. However, state-of-the-art works...Votes: 0GitHub stars: 3
- Embodied Neuromorphic Artificial Intelligence For Robotics Perspectives Challenges And Research Development Stack**arXiv ID:** 2404.03325 **Authors:** Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Fakhreddine Zayer, Jorge Dias, Muhammad Shafique **Published:** 2024-04-04T09:52:22Z **Abstract:** Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging fr...Votes: 0GitHub stars: 3
- Efficient Uncertainty Estimation In Spiking Neural Networks Via Mcdropout**arXiv ID:** 2304.10191 **Authors:** Tao Sun, Bojian Yin, Sander Bohte **Published:** 2023-04-20T10:05:57Z **Abstract:** Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware. As with classical artificial neural networks (ANNs), predictive uncertainties are important for decision making in high-stakes applications, such ...Votes: 0GitHub stars: 3