3 papers
cs.LG2025
STAS: Spatio-Temporal Adaptive Computation Time for Spiking Transformers
Donghwa Kang, Doohyun Kim, Sang-Ki Ko +3
Spiking neural networks (SNNs) offer energy efficiency over artificial neural networks (ANNs) but suffer from high latency and computational overhead due to their multi-timestep op…
eess.SY2025
CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
Woojin Shin, Donghwa Kang, Byeongyun Park +3
Detection Transformers (DETR) are increasingly adopted in autonomous vehicle (AV) perception systems due to their superior accuracy over convolutional networks. However, concurrent…
cs.AI2024
AT-SNN: Adaptive Tokens for Vision Transformer on Spiking Neural Network
Donghwa Kang, Youngmoon Lee, Eun-Kyu Lee +3
In the training and inference of spiking neural networks (SNNs), direct training and lightweight computation methods have been orthogonally developed, aimed at reducing power consu…