Maximizing the Potential of Multiheaded Attention Mechanisms Dynamic Head Allocation Algorithm

Runyuan Bao

2024

Abstract

The Transformer model, pioneered by its novel attention mechanism, marked a significant departure from traditional recurrent and convolutional neural network architectures. This study enhances the Transformer’s multi-head attention mechanism, a key element in its ability to handle complex dependencies and parallel processing. The author introduces the Dynamic Head Allocation Algorithm (DHAA), an innovative approach aimed at optimizing the efficiency and accuracy of Transformer models. DHAA dynamically changes attention heads numbers in response to the complexity of sequences, thereby optimizing computational resource allocation. This adaptive method contrasts with the static allocation commonly used, where the number of heads is uniform across varying inputs. The extensive experiments demonstrate that Transformers augmented with DHAA exhibit notable improvements in training speed and model accuracy, alongside enhanced resource efficiency. These findings not only represent a significant contribution to neural network optimization techniques but also broaden the applicability of Transformer models across diverse machine learning tasks.

Paper Citation

in Harvard Style

Bao R. (2024). Maximizing the Potential of Multiheaded Attention Mechanisms Dynamic Head Allocation Algorithm. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 102-109. DOI: 10.5220/0012826100004547

in Bibtex Style

@conference{icdse24,
author={Runyuan Bao},
title={Maximizing the Potential of Multiheaded Attention Mechanisms Dynamic Head Allocation Algorithm},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={102-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012826100004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Maximizing the Potential of Multiheaded Attention Mechanisms Dynamic Head Allocation Algorithm
SN - 978-989-758-690-3
AU - Bao R.
PY - 2024
SP - 102
EP - 109
DO - 10.5220/0012826100004547
PB - SciTePress