IterL2Norm: Fast Iterative L2-Normalization

Transformer-based large language models are a memory-bound model whose operation is based on a large amount of data that are marginally reused. Thus, the data movement between a host and accelerator likely dictates the total wall-clock time. Layer normalization is one of the key workloads in the tra...

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Bibliographic Details

Published in:	Proceedings - Design, Automation, and Test in Europe Conference and Exhibition pp. 1 - 7
Main Authors:	Ye, ChangMin, Sim, Yonguk, Kim, Youngchae, Jin, SeongMin, Jeong, Doo Seok
Format:	Conference Proceeding
Language:	English
Published:	EDAA 31.03.2025
Subjects:	Convergence Data models fast convergence Iterative algorithms IterL2Norm Large language models layer normalization Semiconductor device modeling Solid modeling Solids Steady-state Transformers Vectors
ISSN:	1558-1101
Online Access:	Get full text
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