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The Ethereal
RTFormer: Re-parameter TSBN Spiking Transformer
June 20, 2024 ยท Declared Dead ยท ๐ IEEE International Joint Conference on Neural Network
"No code URL or promise found in abstract"
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Authors
Hongzhi Wang, Xiubo Liang, Mengjian Li, Tao Zhang
arXiv ID
2406.14180
Category
cs.NE: Neural & Evolutionary
Citations
3
Venue
IEEE International Joint Conference on Neural Network
Last Checked
4 months ago
Abstract
The Spiking Neural Networks (SNNs), renowned for their bio-inspired operational mechanism and energy efficiency, mirror the human brain's neural activity. Yet, SNNs face challenges in balancing energy efficiency with the computational demands of advanced tasks. Our research introduces the RTFormer, a novel architecture that embeds Re-parameterized Temporal Sliding Batch Normalization (TSBN) within the Spiking Transformer framework. This innovation optimizes energy usage during inference while ensuring robust computational performance. The crux of RTFormer lies in its integration of reparameterized convolutions and TSBN, achieving an equilibrium between computational prowess and energy conservation.
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