  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.
