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arXiv · 2505.14022

Towards Efficient Multi-Scale Deformable Attention on NPU

Chenghuan Huang·Zhigeng Xu·Chong Sun·Chen Li·Ziyang Ma
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Abstract

Multi-scale deformable attention (MSDA) is a flexible and powerful feature extraction mechanism for visual tasks, but its random-access grid sampling strategy poses significant optimization challenges, especially on domain-specific accelerators such as NPUs. In this work, we present a co-design approach that systematically rethinks memory access and computation strategies for MSDA on the Ascend NPU architecture. With this co-design approach, our implementation supports both efficient forward and backward computation, is fully adapted for training workloads, and incorporates a suite of hardware-aware optimizations. Extensive experiments show that our solution achieves up to $5.9\times$ (forward), $8.9\times$ (backward), and $7.3\times$ (end-to-end training) speedup over the grid sample-based baseline, and $1.9\times$, $2.4\times$, and $2.0\times$ acceleration over the latest vendor library, respectively.

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Chenghuan Huang, Zhigeng Xu, Chong Sun, Chen Li, Ziyang Ma. 2025-05-20. Towards Efficient Multi-Scale Deformable Attention on NPU. https://arxiv.org/abs/2505.14022

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