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arXiv · 2507.20096

EcoTransformer: Attention without Multiplication

Xin Gao·Xingming Xu·Shirin Amiraslani·Hong Xu
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Abstract

The Transformer, with its scaled dot-product attention mechanism, has become a foundational architecture in modern AI. However, this mechanism is computationally intensive and incurs substantial energy costs. We propose a new Transformer architecture EcoTransformer, in which the output context vector is constructed as the convolution of the values using a Laplacian kernel, where the distances are measured by the L1 metric between the queries and keys. Compared to dot-product based attention, the new attention score calculation is free of matrix multiplication. It performs on par with, or even surpasses, scaled dot-product attention in NLP, bioinformatics, and vision tasks, while consuming significantly less energy. (This version (v2) supersedes v1 and reflects the intended release and licensing.)

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Xin Gao, Xingming Xu, Shirin Amiraslani, Hong Xu. 2025-07-27. EcoTransformer: Attention without Multiplication. https://arxiv.org/abs/2507.20096

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