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Energy-Efficient and Fault-Tolerant Spiking Neural Networks

https://doi.org/10.2139/ssrn.5339085
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inferredcites →https://doi.org/10.1007/978-3-031-20053-3_3openalex-snapshot
Neural Architecture Search for Spiking Neural Networks
inferredcites →https://doi.org/10.1007/978-3-031-73116-7_2openalex-snapshot
Spiking Wavelet Transformer
inferredcites →https://doi.org/10.1007/978-3-319-46493-0_32openalex-snapshot
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s11432-024-4101-6openalex-snapshot
SpikingMiniLM: energy-efficient spiking transformer for natural language understanding
inferredcites →https://doi.org/10.1016/j.knosys.2024.111780openalex-snapshot
Tensor decomposition based attention module for spiking neural networks
inferredcites →https://doi.org/10.1016/j.neucom.2023.02.026openalex-snapshot
Learning rules in spiking neural networks: A survey
inferredcites →https://doi.org/10.1016/j.neucom.2024.127279openalex-snapshot
Spikeformer: Training high-performance spiking neural network with transformer
inferredcites →https://doi.org/10.1016/j.neunet.2009.08.010openalex-snapshot
To spike or not to spike: A probabilistic spiking neuron model
inferredcites →https://doi.org/10.1016/j.neunet.2021.09.022openalex-snapshot
Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing
inferredcites →https://doi.org/10.1016/j.neunet.2024.106172openalex-snapshot
Efficient spiking neural network design via neural architecture search
inferredcites →https://doi.org/10.1016/j.neuron.2021.01.009openalex-snapshot
Visualizing a joint future of neuroscience and neuromorphic engineering
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Spatiotemporal representations in the olfactory system
inferredcites →https://doi.org/10.1016/s0166-2236(03)00034-1openalex-snapshot
Bursts as a unit of neural information: selective communication via resonance
inferredcites →https://doi.org/10.1016/s0893-6080(97)00011-7openalex-snapshot
Networks of spiking neurons: The third generation of neural network models
inferredcites →https://doi.org/10.1038/14731openalex-snapshot
Information theory and neural coding
inferredcites →https://doi.org/10.1038/376033a0openalex-snapshot
Pattern recognition computation using action potential timing for stimulus representation
inferredcites →https://doi.org/10.1038/nature24270openalex-snapshot
Mastering the game of Go without human knowledge
inferredcites →https://doi.org/10.1038/s41565-020-0655-zopenalex-snapshot
Memory devices and applications for in-memory computing
inferredcites →https://doi.org/10.1038/s41586-018-0180-5openalex-snapshot
Equivalent-accuracy accelerated neural-network training using analogue memory
inferredcites →https://doi.org/10.1038/s41586-019-1677-2openalex-snapshot
Towards spike-based machine intelligence with neuromorphic computing
inferredcites →https://doi.org/10.1073/pnas.1604850113openalex-snapshot
Convolutional networks for fast, energy-efficient neuromorphic computing
inferredcites →https://doi.org/10.1088/2634-4386/ac8828openalex-snapshot
Meta-learning spiking neural networks with surrogate gradient descent
inferredcites →https://doi.org/10.1088/2634-4386/ac9b86openalex-snapshot
Beyond classification: directly training spiking neural networks for semantic segmentation
inferredcites →https://doi.org/10.1109/access.2022.3179968openalex-snapshot
Spiking Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/cisce58541.2023.10142563openalex-snapshot
Multi-dimensional Attention Spiking Transformer for Event-based Image Classification
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.761openalex-snapshot
Weighted-Entropy-Based Quantization for Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01357openalex-snapshot
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01101openalex-snapshot
VirTex: Learning Visual Representations from Textual Annotations
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00860openalex-snapshot
Spiking Transformers for Event-based Single Object Tracking
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01212openalex-snapshot
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
inferredcites →https://doi.org/10.1109/cvpr52729.2023.00762openalex-snapshot
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
inferredcites →https://doi.org/10.1109/icassp48485.2024.10445971openalex-snapshot
Autost: Training-Free Neural Architecture Search For Spiking Transformers
inferredcites →https://doi.org/10.1109/iccv48922.2021.00266openalex-snapshot
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00515openalex-snapshot
ReCU: Reviving the Dead Weights in Binary Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00521openalex-snapshot
Improving Low-Precision Network Quantization via Bin Regularization
inferredcites →https://doi.org/10.1109/iccv51070.2023.00169openalex-snapshot
Masked Spiking Transformer
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207239openalex-snapshot
A critical survey of STDP in Spiking Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.1109/iscas.2018.8351295openalex-snapshot
Conversion of analog to spiking neural networks using sparse temporal coding
inferredcites →https://doi.org/10.1109/jproc.2014.2313565openalex-snapshot
Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
inferredcites →https://doi.org/10.1109/jproc.2015.2496679openalex-snapshot
To Spike or Not to Spike: That Is the Question
inferredcites →https://doi.org/10.1109/jproc.2020.3045625openalex-snapshot
Brain-Inspired Learning on Neuromorphic Substrates
inferredcites →https://doi.org/10.1109/jssc.2007.914337openalex-snapshot
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
inferredcites →https://doi.org/10.1109/jssc.2014.2342715openalex-snapshot
A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
inferredcites →https://doi.org/10.1109/mm.2018.112130359openalex-snapshot
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
inferredcites →https://doi.org/10.1109/tcds.2025.3568833openalex-snapshot
SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event Based-Recognition
inferredcites →https://doi.org/10.1109/tcsi.2006.887979openalex-snapshot
AER EAR: A Matched Silicon Cochlea Pair With Address Event Representation Interface
inferredcites →https://doi.org/10.1109/tcsvt.2025.3570056openalex-snapshot
Highly Efficient 3D Human Pose Tracking From Events With Spiking Spatiotemporal Transformer
inferredcites →https://doi.org/10.1109/tetci.2024.3359539openalex-snapshot
IM-LIF: Improved Neuronal Dynamics With Attention Mechanism for Direct Training Deep Spiking Neural Network
inferredcites →https://doi.org/10.1109/tnn.2004.832719openalex-snapshot
Which Model to Use for Cortical Spiking Neurons?
inferredcites →https://doi.org/10.1109/tnnls.2021.3119238openalex-snapshot
Spiking Deep Residual Networks
inferredcites →https://doi.org/10.1109/tnnls.2023.3263008openalex-snapshot
Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/tnnls.2024.3355393openalex-snapshot
Advancing Spiking Neural Networks Toward Deep Residual Learning
inferredcites →https://doi.org/10.1109/tnnls.2024.3370294openalex-snapshot
Enhanced Network Compression Through Tensor Decompositions and Pruning
inferredcites →https://doi.org/10.1109/tpami.2013.71openalex-snapshot
Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets
inferredcites →https://doi.org/10.1109/tpami.2023.3275769openalex-snapshot
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN
inferredcites →https://doi.org/10.1109/tpami.2023.3329173openalex-snapshot
MetaFormer Baselines for Vision
inferredcites →https://doi.org/10.1126/sciadv.1701329openalex-snapshot
Ultralow power artificial synapses using nanotextured magnetic Josephson junctions
inferredcites →https://doi.org/10.1126/science.1254642openalex-snapshot
A million spiking-neuron integrated circuit with a scalable communication network and interface
inferredcites →https://doi.org/10.1145/3387902.3392613openalex-snapshot
Verified instruction-level energy consumption measurement for NVIDIA GPUs
inferredcites →https://doi.org/10.1152/jn.00686.2005openalex-snapshot
Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
inferredcites →https://doi.org/10.1162/08997660152002852openalex-snapshot
Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex
inferredcites →https://doi.org/10.1162/neco.1996.8.1.1openalex-snapshot
Lower Bounds for the Computational Power of Networks of Spiking Neurons
inferredcites →https://doi.org/10.1609/aaai.v34i07.6900openalex-snapshot
Sparsity-Inducing Binarized Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v35i12.17265openalex-snapshot
Near Lossless Transfer Learning for Spiking Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v35i3.16306openalex-snapshot
SA-BNN: State-Aware Binary Neural Network
inferredcites →https://doi.org/10.1609/aaai.v36i1.19874openalex-snapshot
Optimized Potential Initialization for Low-Latency Spiking Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v38i15.29616openalex-snapshot
Memory-Efficient Reversible Spiking Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2019/189openalex-snapshot
STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2023/335openalex-snapshot
Learnable Surrogate Gradient for Direct Training Spiking Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2024/348openalex-snapshot
One-step Spiking Transformer with a Linear Complexity
inferredcites →https://doi.org/10.3389/fnins.2013.00178openalex-snapshot
Real-time classification and sensor fusion with a spiking deep belief network
inferredcites →https://doi.org/10.3389/fnins.2015.00141openalex-snapshot
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses
inferredcites →https://doi.org/10.3389/fnins.2017.00682openalex-snapshot
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification
inferredcites →https://doi.org/10.3389/fnins.2019.00095openalex-snapshot
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
inferredcites →https://doi.org/10.4230/lipics.giscience.2023.43openalex-snapshot
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
inferredcites →https://doi.org/10.48550/arxiv.1103.0398openalex-snapshot
Natural Language Processing (almost) from Scratch
inferredcites →https://doi.org/10.48550/arxiv.1308.3432openalex-snapshot
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
inferredcites →https://doi.org/10.48550/arxiv.1412.4564openalex-snapshot
MatConvNet - Convolutional Neural Networks for MATLAB
inferredcites →https://doi.org/10.48550/arxiv.1510.08829openalex-snapshot
Spiking Deep Networks with LIF Neurons
inferredcites →https://doi.org/10.48550/arxiv.1511.06393openalex-snapshot
Fixed Point Quantization of Deep Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1810.08646openalex-snapshot
SLAYER: Spike Layer Error Reassignment in Time
inferredcites →https://doi.org/10.48550/arxiv.2005.14165openalex-snapshot
Language Models are Few-Shot Learners
inferredcites →https://doi.org/10.48550/arxiv.2007.01204openalex-snapshot
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2103.00476openalex-snapshot
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2106.06984openalex-snapshot
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
inferredcites →https://doi.org/10.48550/arxiv.2201.12738openalex-snapshot
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2205.00459openalex-snapshot
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
inferredcites →https://doi.org/10.48550/arxiv.2209.15425openalex-snapshot
Spikformer: When Spiking Neural Network Meets Transformer
inferredcites →https://doi.org/10.48550/arxiv.2212.13345openalex-snapshot
The Forward-Forward Algorithm: Some Preliminary Investigations
inferredcites →https://doi.org/10.48550/arxiv.2303.04347openalex-snapshot
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2311.16456openalex-snapshot
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
inferredcites →https://doi.org/10.52202/079017-0416openalex-snapshot
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
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