Abstract
Image segmentation is one of the major computer vision tasks, which is applicable in a variety of domains, such as autonomous navigation of an unmanned aerial vehicle. However, image segmentation cannot easily materialize on tiny embedded systems because image segmentation models generally have high peak memory usage due to their architectural characteristics. This work finds that image segmentation models unnecessarily require large memory space with an existing tiny machine learning framework. That is, the existing framework cannot effectively manage the memory space for the image segmentation models. This work proposes TinySeg, a new model optimizing framework that enables memory-efficient image segmentation for tiny embedded systems. TinySeg analyzes the lifetimes of tensors in the target model and identifies long-living tensors. Then, TinySeg optimizes the memory usage of the target model mainly with two methods: (i) tensor spilling into local or remote storage and (ii) fused fetching of spilled tensors. This work implements TinySeg on top of the existing tiny machine learning framework and demonstrates that TinySeg can reduce the peak memory usage of an image segmentation model by 39.3% for tiny embedded systems.
Abstract (translated)
图像分割是计算机视觉中的一个重要任务，适用于各种领域，如无人驾驶无人机的自主导航。然而，由于图像分割模型的架构特点，它们通常具有较高的峰值内存使用率，这使得在小型嵌入系统上实现图像分割变得困难。这项工作发现，与现有的微型机器学习框架相比，图像分割模型不必要的需要大型的内存空间。也就是说，现有的框架无法有效地管理图像分割模型的内存空间。这项工作提出了一种名为TinySeg的新模型优化框架，可以实现对小型嵌入系统的内存高效图像分割。TinySeg分析了目标模型中张量的生命周期，并识别出长期存在的张量。然后，TinySeg通过（i）张量溢出到局部或远地存储和（ii）张量融合获取溢出的张量来优化目标模型的内存使用。这项工作在现有微型机器学习框架上实现了TinySeg，并证明了TinySeg可以在小型嵌入系统上降低图像分割模型的峰值内存使用率39.3%。
URL
https://arxiv.org/abs/2405.01857
