Spiking Wavelet Transformer: Enhancing Frequency Learning in SNNs

Core Concepts

SWformer integrates wavelet transform for high-frequency feature learning in SNNs.

Abstract

The content introduces the Spiking Wavelet Transformer (SWformer) as an alternative to self-attention-based token mixers, emphasizing frequency learning. It proposes a Frequency-Aware Token Mixer (FATM) for comprehensive spatial-frequency feature learning. SWformer outperforms vanilla Spiking Transformers in capturing high-frequency visual components, reducing energy consumption and parameters while improving performance on ImageNet datasets. The architecture includes spiking neuron layers, neuromorphic chips, and spiking vision transformers. Experiments demonstrate SWformer's effectiveness on static and neuromorphic datasets. Introduction: Spiking Neural Networks (SNNs) mimic biological neurons with binary spikes. SNNs offer efficiency but lag behind ANNs in accuracy due to global operations. Incorporating advanced architectures from ANNs enhances SNN performance. Spiking Wavelet Transformer: SWformer integrates wavelet transform for spatial-frequency feature learning. FATM processes input through three branches for comprehensive feature extraction. Negative spike dynamics enhance frequency representation in SNNs. Experiment: SWformer outperforms vanilla Spiking Transformers on ImageNet datasets. Performance improvements seen on both static and neuromorphic datasets. Method Analysis: Visualization shows FATM captures specific frequency information effectively. Number of splitting blocks impacts resource utilization and performance. Firing threshold affects power consumption without compromising accuracy.

Stats

Spiking neural networks offer energy-efficient processing by mimicking brain events. SWformer reduces energy consumption by over 50% compared to vanilla Spiking Transformers.

Quotes

"SWformer captures more high-frequency signals than Spiking Transformers." "Experiments show SWformer's effectiveness in capturing spatial-frequency patterns."

Key Insights Distilled From

Spiking Wavelet Transformer

by Yuetong Fang... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11138.pdf

Deeper Inquiries

どのようにウェーブレット変換の統合がSNNアーキテクチャの将来的な発展に影響を与える可能性がありますか？

ウェーブレット変換は、SNNアーキテクチャに高い周波数情報を取り込むことで、次世代の開発に革新的な影響を与える可能性があります。この統合は、SNNが生物学的な信号処理メカニズムを模倣するため、視覚シーン内の時間依存パターンや高周波成分を効果的に捉えられるようにする点で重要です。ウェーブレット変換はスパース性とロバスト性を持ち、信号投影や特徴抽出において優れた能力を示すため、SNNアーキテクチャ全体の精度向上やエネルギー効率化へ貢献することが期待されます。