MA-SNN的优势体现在三个方面:第一,通过注意机制优化膜电位可以导致更少的脉冲响应,同时得到更好的性能和能效;第二,在效率方面,MA自适应抑制背景噪声的膜电位,使这些脉冲神经元不被激活,这解释了为什么在MA-SNN中可以实现更低的脉冲活动率;第三,作者证明了当对MS-Res-SNN加入注意机制时,可以解决一般深度SNN存在的...
Moreover, compressed SNN models generated by our methods can have up to 12.2x better compute energy-efficiency compared to ANNs that have a similar number of parameters. 对芯片边缘智能的需求不断增长,这促使我们探索算法技术和专用硬件,以降低当前机器学习模型的计算能量。深度脉冲神经网络 (SNN) 特别...
其中λ和α是固定数值(分别为 1.0507 和 1.6726)。这些值背后的推论(零均值/单位方差)构成了自归一化神经网络的基础(SNN)。 11. SReLU S 型整流线性激活单元(S-shaped Rectified Linear Activation Unit,SReLU)属于以 ReLU 为代表的整流激活函数族。它由三个分段线性函数组成。其中两种函数的斜度,以及函数相交的...
python Att_SNN_CNN.py View the results in /MA_SNN/DVSGestures/CNN/Result/. 2. CIFAR10-DVS DownloadCIFAR10-DVSand processing dataset using official matlab program, then put the result to /MA_SNN/CIFAR10DVS/data. MA_SNN ├── /CIFAR10DVS/ │ ├── /data/ │ │ ├── /airplane/...
STSC-SNN: Spatio-Temporal Synaptic Connection with temporal convolution and attention for spiking neural networks - Tab-ct/STSC-SNN
这是一篇在SNN的时间维度上引入attention的文章,中了2021年的ICCV。 论文传送门:ICCV 2021 Open Access Repository Abstract 由于事件(events)通常是稀疏和非均匀的,并且具有µs的时间分辨率,所以如何有效和高效地处理时空事件流具有很大的价值。SNN具有从事件流中提取有效时空特征的潜力。将单个事件聚合为更高时间分辨...
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks [TNNLS 2024] How to Run First clone the repository. git clone https://github.com/ridgerchu/TCJA cd TCJA pip install -r requirements.txt Train DVS128 Detailed usage of the script could be found in the source file. python...
STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks Spiking Neural Networks (SNNs) have shown great promise in processing spatiotemporal information compared to Artificial Neural Networks (ANNs). However, th... X Wu,Y Song,Y Zhou,... - 《Frontiers in Neur...
Extensive experiments show that the A2OS2A-based Spiking Transformer outperforms existing SNN-based Transformers on several datasets, even achieving an accuracy of 78.66\% on ImageNet-1K. Our work represents a significant advancement in SNN-based Transformer models, offering a more accurate and ...
Spikingformerzhou2023spikingformer SNN Spikingformer-8-512 2242 29.68 7.46 4 74.79 Spikingformerzhou2023spikingformer SNN Spikingformer-8-768 2242 66.34 13.68 4 75.85 S-Transformeryao2023spikedriven SNN S-Transformer-8-384 2242 16.81 3.90 4 72.28 S-Transformeryao2023spikedriven SNN S-Transformer-8-...