Presentation
TinySeg: Memory-efficient Image Segmentation for Small Embedded Systems
DescriptionImage 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 small 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. Therefore, the existing tiny machine learning framework cannot effectively manage the memory space for the image segmentation models.
This work proposes TinySeg, a new optimizing framework that enables memory-efficient image segmentation for small 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 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 percent for small embedded systems.
This work proposes TinySeg, a new optimizing framework that enables memory-efficient image segmentation for small 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 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 percent for small embedded systems.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security