Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones

• URL: http://arxiv.org/abs/2603.26551v1
• Date: Fri, 27 Mar 2026 16:05:20 GMT
• Title: Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones
• Abstract summary: We present LowFormer, a novel vision backbone family.<n>LowFormer features a streamlined macro and micro design that includes Lowtention, a lightweight alternative to Multi-Head Self-Attention.<n>We demonstrate LowFormer's wide applicability by evaluating it on smaller image classification datasets, as well as adapting it to several downstream tasks, such as object detection, semantic segmentation, image retrieval, and visual object tracking.
• Score: 8.982222466965384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision backbone networks play a central role in modern computer vision. Enhancing their efficiency directly benefits a wide range of downstream applications. To measure efficiency, many publications rely on MACs (Multiply Accumulate operations) as a predictor of execution time. In this paper, we experimentally demonstrate the shortcomings of such a metric, especially in the context of edge devices. By contrasting the MAC count and execution time of common architectural design elements, we identify key factors for efficient execution and provide insights to optimize backbone design. Based on these insights, we present LowFormer, a novel vision backbone family. LowFormer features a streamlined macro and micro design that includes Lowtention, a lightweight alternative to Multi-Head Self-Attention. Lowtention not only proves more efficient, but also enables superior results on ImageNet. Additionally, we present an edge GPU version of LowFormer, that can further improve upon its baseline's speed on edge GPU and desktop GPU. We demonstrate LowFormer's wide applicability by evaluating it on smaller image classification datasets, as well as adapting it to several downstream tasks, such as object detection, semantic segmentation, image retrieval, and visual object tracking. LowFormer models consistently achieve remarkable speed-ups across various hardware platforms compared to recent state-of-the-art backbones. Code and models are available at https://github.com/altair199797/LowFormer/blob/main/Beyond_MACs.md.

Related papers

• Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement [50.93686436282772]
  We aim to delve into the limits of image enhancers both from visual quality and computational efficiency.<n>By rethinking the task demands, we build an explicit connection, i.e., visual quality and computational efficiency are corresponding to model learning and structure design.<n>Ultimately, this achieves efficient low-light image enhancement using only a single convolutional layer, while maintaining excellent visual quality.
  arXiv  Detail & Related papers  (2025-02-27T08:20:03Z)
• UniForm: A Reuse Attention Mechanism Optimized for Efficient Vision Transformers on Edge Devices [1.795366746592388]
  Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging.<n>We introduce a novel Reuse Attention mechanism, tailored for efficient memory access and computational optimization.
  arXiv  Detail & Related papers  (2024-12-03T10:04:15Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones [10.435069781620957]
  Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. We analyze common modules and architectural design choices for backbones not in terms of MACs, but rather in actual throughput and latency. We combine both macro and micro design to create a new family of hardware-efficient backbone networks called LowFormer.
  arXiv  Detail & Related papers  (2024-09-05T12:18:32Z)
• Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks [64.39488944424095]
  We present the first machine learning approach to generate low-discrepancy point sets named Message-Passing Monte Carlo points. MPMC points are empirically shown to be either optimal or near-optimal with respect to the discrepancy for low dimension and small number of points.
  arXiv  Detail & Related papers  (2024-05-23T21:17:20Z)
• ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels [1.304892050913381]
  We introduce a new graph-based program representation for parallel applications that extends the Abstract Syntax Tree. We evaluate our proposed representation by training a Graph Neural Network (GNN) to predict the runtime of an OpenMP code region. Results show that our approach is indeed effective and has normalized RMSE as low as 0.004 to at most 0.01 in its runtime predictions.
  arXiv  Detail & Related papers  (2023-04-07T05:52:59Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction [67.11722682878722]
  This work presents EfficientViT, a new family of high-resolution vision models with novel multi-scale linear attention. Our multi-scale linear attention achieves the global receptive field and multi-scale learning. EfficientViT delivers remarkable performance gains over previous state-of-the-art models.
  arXiv  Detail & Related papers  (2022-05-29T20:07:23Z)
• MAPLE-Edge: A Runtime Latency Predictor for Edge Devices [80.01591186546793]
  We propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes.
  arXiv  Detail & Related papers  (2022-04-27T14:00:48Z)
• An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments [20.485491385050615]
  Low-overhead VPR techniques would enable platforms equipped with low-end, cheap hardware. Our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations.
  arXiv  Detail & Related papers  (2021-09-22T19:01:20Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.