SpikingResformer: Bridging ResNet and Vision Transformer in SNNs

Kernkonzepte

Proposing a novel spiking self-attention mechanism, Dual Spike Self-Attention (DSSA), and introducing SpikingResformer architecture to enhance performance and energy efficiency in SNNs.

Zusammenfassung

The content introduces the concept of SpikingResformer, combining ResNet-based architecture with DSSA for improved performance. It addresses challenges in incorporating transformer structures into SNNs and presents experimental results showcasing superior accuracy, fewer parameters, and lower energy consumption compared to existing methods.

Introduction

Discusses the advantages of Spiking Neural Networks (SNNs) over ANNs.
Highlights the interest in incorporating self-attention mechanisms from Vision Transformers into SNNs.

Dual Spike Self-Attention

Introduces DSSA as a novel spiking self-attention mechanism compatible with SNNs.
Details the scaling factors employed in DSSA for handling feature maps of arbitrary scales.

SpikingResformer Architecture

Describes the architecture combining ResNet-based multi-stage design with DSSA.
Outlines experimental results showing improved accuracy, reduced parameters, and energy consumption compared to existing methods.

Experiments

Evaluates SpikingResformer on ImageNet classification task.
Conducts ablation studies on key components like multi-stage architecture, group-wise convolution layer, and DSSA.
Explores transfer learning ability on static datasets like CIFAR10 and CIFAR100, as well as neuromorphic datasets like CIFAR10-DVS and DVSGesture.

Conclusion

Summarizes the contributions of proposing DSSA and SpikingResformer for enhancing performance in SNNs.

Statistiken

Notably, our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps.
Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking Vision Transformer counterparts.

Zitate

"The main contributions of this paper can be summarized as follows:"
"We propose the Dual Spike Self-Attention (DSSA), a novel spiking self-attention mechanism."
"Experimental results show that our proposed Spik-ingResformer significantly outperforms other spik-ing Vision Transformer counterparts."

Wichtige Erkenntnisse aus

SpikingResformer

by Xinyu Shi,Ze... um arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14302.pdf

Tiefere Fragen

How does the introduction of transformer architectures into SNNs impact their performance?

The introduction of transformer architectures into Spiking Neural Networks (SNNs) has a significant impact on their performance. Vision Transformers have shown remarkable success in Artificial Neural Networks (ANNs), leading to a growing interest in incorporating self-attention mechanisms and transformer-based architecture into SNNs. This integration allows SNNs to benefit from the global context understanding and feature extraction capabilities that transformers offer, improving their performance on challenging vision tasks. By leveraging the self-attention mechanism, SNNs can effectively capture long-range dependencies and relationships within input data, enhancing their ability to process complex visual information.

What are the implications of using a spike-driven characteristic in neural networks?

Utilizing a spike-driven characteristic in neural networks has several implications:

Efficiency: Spike-driven networks operate based on sparse spikes rather than continuous values, leading to energy-efficient computation as only relevant neurons are activated.
Biological Plausibility: Mimicking the behavior of biological neurons that communicate through spikes enhances the realism and neuroscientific relevance of artificial neural models.
Event-Based Processing: Spike-driven characteristics enable event-based processing where computations occur only when there is an input spike, allowing for asynchronous and low-latency operations.
Scalability: Spike-driven networks can scale efficiently due to their sparse nature, making them suitable for large-scale applications without significantly increasing computational resources.

How might transfer learning abilities differ between static image datasets and neuromorphic datasets?

Transfer learning abilities may vary between static image datasets and neuromorphic datasets due to differences in data representation and domain characteristics:

Static Image Datasets:

Transfer learning from pre-trained models on static image datasets like CIFAR10 or ImageNet tends to be effective for similar tasks or domains with comparable features.
The hierarchical structure of CNN features learned from static images can be transferred successfully across related tasks.

Neuromorphic Datasets:

Neuromorphic datasets often involve temporal dynamics captured by event streams rather than traditional pixel values found in static images.
Transfer learning from pre-trained models on static images may not directly translate well to neuromorphic datasets due to differences in data format and underlying representations.
Models trained on one type of dataset may struggle with capturing temporal patterns inherent in neuromorphic data unless specifically adapted or fine-tuned for such inputs.

In summary, while transfer learning can be effective within similar domains or task types regardless of dataset type, adapting pre-trained models between static image datasets and neuromorphic datasets requires careful consideration of data format, representation differences, and domain-specific characteristics for successful knowledge transfer.