AToM: Adaptive Token Merging for Efficient Acceleration of Vision Transformer

Recently, Vision Transformers (ViTs) have set a new standard in computer vision (CV), showing unparalleled image processing performance. However, their substantial computational requirements hinder practical deployment, especially on resource-limited devices common in CV applications. Token merging...

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Veröffentlicht in:	IEEE transactions on computers Jg. 74; H. 5; S. 1620 - 1633
Hauptverfasser:	Shin, Jaekang, Kang, Myeonggu, Han, Yunki, Park, Junyoung, Kim, Lee-Sup
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2025
Schlagworte:	algorithm-architecture co-design Computational efficiency Computational modeling Computer architecture Computer vision Computers DNN accelerator Graphics processing units Hardware Heuristic algorithms Merging token merge transformer-based computer vision Transformers
ISSN:	0018-9340, 1557-9956
Online-Zugang:	Volltext
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