UniForm: A Reuse Attention Mechanism Optimized for Efficient Vision Transformers on Edge Devices

• URL: http://arxiv.org/abs/2412.02344v1
• Date: Tue, 03 Dec 2024 10:04:15 GMT
• Title: UniForm: A Reuse Attention Mechanism Optimized for Efficient Vision Transformers on Edge Devices
• Authors: Seul-Ki Yeom, Tae-Ho Kim,
• Abstract summary: Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging. We introduce a novel Reuse Attention mechanism, tailored for efficient memory access and computational optimization.
• Score: 1.795366746592388
• License:
• Abstract: Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging due to high memory and computational demands. In this paper, we introduce a novel Reuse Attention mechanism, tailored for efficient memory access and computational optimization, enabling seamless operation on resource-constrained platforms without compromising performance. Unlike traditional multi-head attention (MHA), which redundantly computes separate attention matrices for each head, Reuse Attention consolidates these computations into a shared attention matrix, significantly reducing memory overhead and computational complexity. Comprehensive experiments on ImageNet-1K and downstream tasks show that the proposed UniForm models leveraging Reuse Attention achieve state-of-the-art imagenet classification accuracy while outperforming existing attention mechanisms, such as Linear Attention and Flash Attention, in inference speed and memory scalability. Notably, UniForm-l achieves a 76.7% Top-1 accuracy on ImageNet-1K with 21.8ms inference time on edge devices like the Jetson AGX Orin, representing up to a 5x speedup over competing benchmark methods. These results demonstrate the versatility of Reuse Attention across high-performance GPUs and edge platforms, paving the way for broader real-time applications

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