2021/05/03 by Juliano S. Assine, Assine, Juliano S., José Cândido Silveira Santos Filho +3
Computer Science · Engineering · #Advanced Neural Network Applications #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2105.00591
openalex publication_date 2021/05/03 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
In the past few years, mobile deep-learning deployment progressed by leaps and bounds, but solutions still struggle to accommodate its severe and fluctuating operational restrictions, which include bandwidth, latency, computation, and energy. In this work, we help to bridge that gap, introducing the first configurable solution for object detection that manages the triple communication-computation-accuracy trade-off with a single set of weights. Our solution shows state-of-the-art results on COCO-2017, adding only a minor penalty on the base EfficientDet-D2 architecture. Our design is robust to the choice of base architecture and compressor and should adapt well for future architectures.