MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers

• URL: http://arxiv.org/abs/2605.10148v1
• Date: Mon, 11 May 2026 07:54:27 GMT
• Title: MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers
• Abstract summary: Vision Transformer (ViT) achieves remarkable accuracy across visual tasks but remains computationally expensive for edge deployment.<n>This paper presents MicroViTv2, a lightweight Vision Transformer optimized for real-device efficiency.
• Score: 6.427162946484909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Vision Transformer (ViT) achieves remarkable accuracy across visual tasks but remains computationally expensive for edge deployment. This paper presents MicroViTv2, a lightweight Vision Transformer optimized for real-device efficiency. Built upon the original MicroViT, the proposed model is designed based on reparameterized design, specifically Reparameterized Patch Embedding (RepEmbed) and Reparameterized Depth-Wise convolution mixer (RepDW) for faster inference, and introduces the Single Depth-Wise Transposed Attention (SDTA) to capture long-range dependencies with minimal redundancy. Despite slightly higher FLOPs, MicroViTv2 improves accuracy up to 0.5% compared to its predecessor and surpassing MobileViTv2, EdgeNeXt, and EfficientViT while maintaining fast inference and high energy efficiency on Jetson AGX Orin. Experiments on ImageNet-1K and COCO demonstrate that hardware-aware design and structural re-parameterization are key to achieving high accuracy and low energy consumption, validating the need to evaluate efficiency beyond FLOPs. Code is available at https://github.com/novendrastywn/MicroViT.

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