A Multi-task Supervised Compression Model for Split Computing
This addresses the challenge of efficient multi-task learning for resource-constrained edge devices, offering a novel solution with significant performance improvements.
The paper tackled the problem of applying split computing to multi-task deep learning, which degrades accuracy and increases latency in existing methods, by proposing Ladon, a multi-task supervised compression model. The results showed it outperformed or rivaled lightweight baselines on ILSVRC 2012, COCO 2017, and PASCAL VOC 2012 datasets, while reducing end-to-end latency by up to 95.4% and energy consumption by up to 88.2%.
Split computing ($\neq$ split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wirelessly connected to stronger edge servers through channels with limited communication capacity. State-of-theart work on split computing presents methods for single tasks such as image classification, object detection, or semantic segmentation. The application of existing methods to multitask problems degrades model accuracy and/or significantly increase runtime latency. In this study, we propose Ladon, the first multi-task-head supervised compression model for multi-task split computing. Experimental results show that the multi-task supervised compression model either outperformed or rivaled strong lightweight baseline models in terms of predictive performance for ILSVRC 2012, COCO 2017, and PASCAL VOC 2012 datasets while learning compressed representations at its early layers. Furthermore, our models reduced end-to-end latency (by up to 95.4%) and energy consumption of mobile devices (by up to 88.2%) in multi-task split computing scenarios.