arXiv - CS - Neural and Evolutionary Computing最新文献_第5页

Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing 在神经形态边缘计算中模拟类脑快速学习

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-28 DOI: arxiv-2408.15800

Kenneth Stewart, Michael Neumeier, Sumit Bam Shrestha, Garrick Orchard, Emre Neftci

{"title":"Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing","authors":"Kenneth Stewart, Michael Neumeier, Sumit Bam Shrestha, Garrick Orchard, Emre Neftci","doi":"arxiv-2408.15800","DOIUrl":"https://doi.org/arxiv-2408.15800","url":null,"abstract":"Achieving personalized intelligence at the edge with real-time learning\u0000capabilities holds enormous promise in enhancing our daily experiences and\u0000helping decision making, planning, and sensing. However, efficient and reliable\u0000edge learning remains difficult with current technology due to the lack of\u0000personalized data, insufficient hardware capabilities, and inherent challenges\u0000posed by online learning. Over time and across multiple developmental stages, the brain has evolved to\u0000efficiently incorporate new knowledge by gradually building on previous\u0000knowledge. In this work, we emulate the multiple stages of learning with\u0000digital neuromorphic technology that simulates the neural and synaptic\u0000processes of the brain using two stages of learning. First, a meta-training\u0000stage trains the hyperparameters of synaptic plasticity for one-shot learning\u0000using a differentiable simulation of the neuromorphic hardware. This\u0000meta-training process refines a hardware local three-factor synaptic plasticity\u0000rule and its associated hyperparameters to align with the trained task domain.\u0000In a subsequent deployment stage, these optimized hyperparameters enable fast,\u0000data-efficient, and accurate learning of new classes. We demonstrate our\u0000approach using event-driven vision sensor data and the Intel Loihi neuromorphic\u0000processor with its plasticity dynamics, achieving real-time one-shot learning\u0000of new classes that is vastly improved over transfer learning. Our methodology\u0000can be deployed with arbitrary plasticity models and can be applied to\u0000situations demanding quick learning and adaptation at the edge, such as\u0000navigating unfamiliar environments or learning unexpected categories of data\u0000through user engagement.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models SpikingSSMs：利用稀疏并行尖峰状态空间模型学习长序列

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-27 DOI: arxiv-2408.14909

Shuaijie Shen, Chao Wang, Renzhuo Huang, Yan Zhong, Qinghai Guo, Zhichao Lu, Jianguo Zhang, Luziwei Leng

{"title":"SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models","authors":"Shuaijie Shen, Chao Wang, Renzhuo Huang, Yan Zhong, Qinghai Guo, Zhichao Lu, Jianguo Zhang, Luziwei Leng","doi":"arxiv-2408.14909","DOIUrl":"https://doi.org/arxiv-2408.14909","url":null,"abstract":"Known as low energy consumption networks, spiking neural networks (SNNs) have\u0000gained a lot of attention within the past decades. While SNNs are increasing\u0000competitive with artificial neural networks (ANNs) for vision tasks, they are\u0000rarely used for long sequence tasks, despite their intrinsic temporal dynamics.\u0000In this work, we develop spiking state space models (SpikingSSMs) for long\u0000sequence learning by leveraging on the sequence learning abilities of state\u0000space models (SSMs). Inspired by dendritic neuron structure, we hierarchically\u0000integrate neuronal dynamics with the original SSM block, meanwhile realizing\u0000sparse synaptic computation. Furthermore, to solve the conflict of event-driven\u0000neuronal dynamics with parallel computing, we propose a light-weight surrogate\u0000dynamic network which accurately predicts the after-reset membrane potential\u0000and compatible to learnable thresholds, enabling orders of acceleration in\u0000training speed compared with conventional iterative methods. On the long range\u0000arena benchmark task, SpikingSSM achieves competitive performance to\u0000state-of-the-art SSMs meanwhile realizing on average 90% of network sparsity.\u0000On language modeling, our network significantly surpasses existing spiking\u0000large language models (spikingLLMs) on the WikiText-103 dataset with only a\u0000third of the model size, demonstrating its potential as backbone architecture\u0000for low computation cost LLMs.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning 距离前向学习：增强前向算法，实现高性能片上学习

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-27 DOI: arxiv-2408.14925

Yujie Wu, Siyuan Xu, Jibin Wu, Lei Deng, Mingkun Xu, Qinghao Wen, Guoqi Li

{"title":"Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning","authors":"Yujie Wu, Siyuan Xu, Jibin Wu, Lei Deng, Mingkun Xu, Qinghao Wen, Guoqi Li","doi":"arxiv-2408.14925","DOIUrl":"https://doi.org/arxiv-2408.14925","url":null,"abstract":"The Forward-Forward (FF) algorithm was recently proposed as a local learning\u0000method to address the limitations of backpropagation (BP), offering biological\u0000plausibility along with memory-efficient and highly parallelized computational\u0000benefits. However, it suffers from suboptimal performance and poor\u0000generalization, largely due to inadequate theoretical support and a lack of\u0000effective learning strategies. In this work, we reformulate FF using distance\u0000metric learning and propose a distance-forward algorithm (DF) to improve FF\u0000performance in supervised vision tasks while preserving its local computational\u0000properties, making it competitive for efficient on-chip learning. To achieve\u0000this, we reinterpret FF through the lens of centroid-based metric learning and\u0000develop a goodness-based N-pair margin loss to facilitate the learning of\u0000discriminative features. Furthermore, we integrate layer-collaboration local\u0000update strategies to reduce information loss caused by greedy local parameter\u0000updates. Our method surpasses existing FF models and other advanced local\u0000learning approaches, with accuracies of 99.7% on MNIST, 88.2% on CIFAR-10,\u000059% on CIFAR-100, 95.9% on SVHN, and 82.5% on ImageNette, respectively.\u0000Moreover, it achieves comparable performance with less than 40% memory cost\u0000compared to BP training, while exhibiting stronger robustness to multiple types\u0000of hardware-related noise, demonstrating its potential for online learning and\u0000energy-efficient computation on neuromorphic chips.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing PMSN：用于多尺度时态处理的并行多室尖峰神经元

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-27 DOI: arxiv-2408.14917

Xinyi Chen, Jibin Wu, Chenxiang Ma, Yinsong Yan, Yujie Wu, Kay Chen Tan

{"title":"PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing","authors":"Xinyi Chen, Jibin Wu, Chenxiang Ma, Yinsong Yan, Yujie Wu, Kay Chen Tan","doi":"arxiv-2408.14917","DOIUrl":"https://doi.org/arxiv-2408.14917","url":null,"abstract":"Spiking Neural Networks (SNNs) hold great potential to realize\u0000brain-inspired, energy-efficient computational systems. However, current SNNs\u0000still fall short in terms of multi-scale temporal processing compared to their\u0000biological counterparts. This limitation has resulted in poor performance in\u0000many pattern recognition tasks with information that varies across different\u0000timescales. To address this issue, we put forward a novel spiking neuron model\u0000called Parallel Multi-compartment Spiking Neuron (PMSN). The PMSN emulates\u0000biological neurons by incorporating multiple interacting substructures and\u0000allows for flexible adjustment of the substructure counts to effectively\u0000represent temporal information across diverse timescales. Additionally, to\u0000address the computational burden associated with the increased complexity of\u0000the proposed model, we introduce two parallelization techniques that decouple\u0000the temporal dependencies of neuronal updates, enabling parallelized training\u0000across different time steps. Our experimental results on a wide range of\u0000pattern recognition tasks demonstrate the superiority of PMSN. It outperforms\u0000other state-of-the-art spiking neuron models in terms of its temporal\u0000processing capacity, training speed, and computation cost. Specifically,\u0000compared with the commonly used Leaky Integrate-and-Fire neuron, PMSN offers a\u0000simulation acceleration of over 10 $times$ and a 30 % improvement in accuracy\u0000on Sequential CIFAR10 dataset, while maintaining comparable computational cost.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks 生物启发尖峰神经网络的研究进展和新范例

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-26 DOI: arxiv-2408.13996

Tianyu Zheng, Liyuan Han, Tielin Zhang

{"title":"Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks","authors":"Tianyu Zheng, Liyuan Han, Tielin Zhang","doi":"arxiv-2408.13996","DOIUrl":"https://doi.org/arxiv-2408.13996","url":null,"abstract":"Spiking neural networks (SNNs) are gaining popularity in the computational\u0000simulation and artificial intelligence fields owing to their biological\u0000plausibility and computational efficiency. This paper explores the historical\u0000development of SNN and concludes that these two fields are intersecting and\u0000merging rapidly. Following the successful application of Dynamic Vision Sensors\u0000(DVS) and Dynamic Audio Sensors (DAS), SNNs have found some proper paradigms,\u0000such as continuous visual signal tracking, automatic speech recognition, and\u0000reinforcement learning for continuous control, that have extensively supported\u0000their key features, including spike encoding, neuronal heterogeneity, specific\u0000functional circuits, and multiscale plasticity. Compared to these real-world\u0000paradigms, the brain contains a spiking version of the biology-world paradigm,\u0000which exhibits a similar level of complexity and is usually considered a mirror\u0000of the real world. Considering the projected rapid development of invasive and\u0000parallel Brain-Computer Interface (BCI), as well as the new BCI-based paradigms\u0000that include online pattern recognition and stimulus control of biological\u0000spike trains, SNNs naturally leverage their advantages in energy efficiency,\u0000robustness, and flexibility. The biological brain has inspired the present\u0000study of SNNs and effective SNN machine-learning algorithms, which can help\u0000enhance neuroscience discoveries in the brain by applying them to the new BCI\u0000paradigm. Such two-way interactions with positive feedback can accelerate brain\u0000science research and brain-inspired intelligence technology.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Uncertainty with Implicit Quantile Network 利用隐含量子网络估算不确定性

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-26 DOI: arxiv-2408.14525

Yi Hung Lim

引用次数: 0

Discovering Long-Term Effects on Parameter Efficient Fine-tuning 发现参数高效微调的长期影响

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-24 DOI: arxiv-2409.06706

Gaole Dai, Yiming Tang, Chunkai Fan, Qizhe Zhang, Zhi Zhang, Yulu Gan, Chengqing Zeng, Shanghang Zhang, Tiejun Huang

{"title":"Discovering Long-Term Effects on Parameter Efficient Fine-tuning","authors":"Gaole Dai, Yiming Tang, Chunkai Fan, Qizhe Zhang, Zhi Zhang, Yulu Gan, Chengqing Zeng, Shanghang Zhang, Tiejun Huang","doi":"arxiv-2409.06706","DOIUrl":"https://doi.org/arxiv-2409.06706","url":null,"abstract":"Pre-trained Artificial Neural Networks (ANNs) exhibit robust pattern\u0000recognition capabilities and share extensive similarities with the human brain,\u0000specifically Biological Neural Networks (BNNs). We are particularly intrigued\u0000by these models' ability to acquire new knowledge through fine-tuning. In this\u0000regard, Parameter-efficient Fine-tuning (PEFT) has gained widespread adoption\u0000as a substitute for full fine-tuning due to its cost reduction in training and\u0000mitigation of over-fitting risks by limiting the number of trainable parameters\u0000during adaptation. Since both ANNs and BNNs propagate information\u0000layer-by-layer, a common analogy can be drawn: weights in ANNs represent\u0000synapses in BNNs, while features (also known as latent variables or logits) in\u0000ANNs represent neurotransmitters released by neurons in BNNs. Mainstream PEFT\u0000methods aim to adjust feature or parameter values using only a limited number\u0000of trainable parameters (usually less than 1% of the total parameters), yet\u0000achieve surprisingly good results. Building upon this clue, we delve deeper\u0000into exploring the connections between feature adjustment and parameter\u0000adjustment, resulting in our proposed method Synapses & Neurons (SAN) that\u0000learns scaling matrices for features and propagates their effects towards\u0000posterior weight matrices. Our approach draws strong inspiration from\u0000well-known neuroscience phenomena - Long-term Potentiation (LTP) and Long-term\u0000Depression (LTD), which also reveal the relationship between synapse\u0000development and neurotransmitter release levels. We conducted extensive\u0000comparisons of PEFT on 26 datasets using attention-based networks as well as\u0000convolution-based networks, leading to significant improvements compared to\u0000other tuning methods (+8.5% over fully-finetune, +7% over Visual Prompt Tuning,\u0000and +3.2% over LoRA). The codes would be released.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models VFM-Det：通过大型基础模型实现高性能车辆检测

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-23 DOI: arxiv-2408.13031

Wentao Wu, Fanghua Hong, Xiao Wang, Chenglong Li, Jin Tang

{"title":"VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models","authors":"Wentao Wu, Fanghua Hong, Xiao Wang, Chenglong Li, Jin Tang","doi":"arxiv-2408.13031","DOIUrl":"https://doi.org/arxiv-2408.13031","url":null,"abstract":"Existing vehicle detectors are usually obtained by training a typical\u0000detector (e.g., YOLO, RCNN, DETR series) on vehicle images based on a\u0000pre-trained backbone (e.g., ResNet, ViT). Some researchers also exploit and\u0000enhance the detection performance using pre-trained large foundation models.\u0000However, we think these detectors may only get sub-optimal results because the\u0000large models they use are not specifically designed for vehicles. In addition,\u0000their results heavily rely on visual features, and seldom of they consider the\u0000alignment between the vehicle's semantic information and visual\u0000representations. In this work, we propose a new vehicle detection paradigm\u0000based on a pre-trained foundation vehicle model (VehicleMAE) and a large\u0000language model (T5), termed VFM-Det. It follows the region proposal-based\u0000detection framework and the features of each proposal can be enhanced using\u0000VehicleMAE. More importantly, we propose a new VAtt2Vec module that predicts\u0000the vehicle semantic attributes of these proposals and transforms them into\u0000feature vectors to enhance the vision features via contrastive learning.\u0000Extensive experiments on three vehicle detection benchmark datasets thoroughly\u0000proved the effectiveness of our vehicle detector. Specifically, our model\u0000improves the baseline approach by $+5.1%$, $+6.2%$ on the $AP_{0.5}$,\u0000$AP_{0.75}$ metrics, respectively, on the Cityscapes dataset.The source code of\u0000this work will be released at https://github.com/Event-AHU/VFM-Det.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks 用于时态网络动态链接预测的对比表征学习

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-22 DOI: arxiv-2408.12753

Amirhossein Nouranizadeh, Fatemeh Tabatabaei Far, Mohammad Rahmati

{"title":"Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks","authors":"Amirhossein Nouranizadeh, Fatemeh Tabatabaei Far, Mohammad Rahmati","doi":"arxiv-2408.12753","DOIUrl":"https://doi.org/arxiv-2408.12753","url":null,"abstract":"Evolving networks are complex data structures that emerge in a wide range of\u0000systems in science and engineering. Learning expressive representations for\u0000such networks that encode their structural connectivity and temporal evolution\u0000is essential for downstream data analytics and machine learning applications.\u0000In this study, we introduce a self-supervised method for learning\u0000representations of temporal networks and employ these representations in the\u0000dynamic link prediction task. While temporal networks are typically\u0000characterized as a sequence of interactions over the continuous time domain,\u0000our study focuses on their discrete-time versions. This enables us to balance\u0000the trade-off between computational complexity and precise modeling of the\u0000interactions. We propose a recurrent message-passing neural network\u0000architecture for modeling the information flow over time-respecting paths of\u0000temporal networks. The key feature of our method is the contrastive training\u0000objective of the model, which is a combination of three loss functions: link\u0000prediction, graph reconstruction, and contrastive predictive coding losses. The\u0000contrastive predictive coding objective is implemented using infoNCE losses at\u0000both local and global scales of the input graphs. We empirically show that the\u0000additional self-supervised losses enhance the training and improve the model's\u0000performance in the dynamic link prediction task. The proposed method is tested\u0000on Enron, COLAB, and Facebook datasets and exhibits superior results compared\u0000to existing models.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Spiking Neural Networks with Hybrid Coding 混合编码的自适应尖峰神经网络

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-22 DOI: arxiv-2408.12407

Huaxu He

{"title":"Adaptive Spiking Neural Networks with Hybrid Coding","authors":"Huaxu He","doi":"arxiv-2408.12407","DOIUrl":"https://doi.org/arxiv-2408.12407","url":null,"abstract":"The Spiking Neural Network (SNN), due to its unique spiking-driven nature, is\u0000a more energy-efficient and effective neural network compared to Artificial\u0000Neural Networks (ANNs). The encoding method directly influences the overall\u0000performance of the network, and currently, direct encoding is primarily used\u0000for directly trained SNNs. When working with static image datasets, direct\u0000encoding inputs the same feature map at every time step, failing to fully\u0000exploit the spatiotemporal properties of SNNs. While temporal encoding converts\u0000input data into spike trains with spatiotemporal characteristics, traditional\u0000SNNs utilize the same neurons when processing input data across different time\u0000steps, limiting their ability to integrate and utilize spatiotemporal\u0000information effectively.To address this, this paper employs temporal encoding\u0000and proposes the Adaptive Spiking Neural Network (ASNN), enhancing the\u0000utilization of temporal encoding in conventional SNNs. Additionally, temporal\u0000encoding is less frequently used because short time steps can lead to\u0000significant loss of input data information, often necessitating a higher number\u0000of time steps in practical applications. However, training large SNNs with long\u0000time steps is challenging due to hardware constraints. To overcome this, this\u0000paper introduces a hybrid encoding approach that not only reduces the required\u0000time steps for training but also continues to improve the overall network\u0000performance.Notably, significant improvements in classification performance are\u0000observed on both Spikformer and Spiking ResNet architectures.our code is\u0000available at https://github.com/hhx0320/ASNN","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0