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.

翻译：在过去几年里,移动深造部署通过飞跃和交错进展,但解决方案仍然难以适应其严重和波动的操作限制,包括带宽、延时、计算和能源。 在这项工作中,我们帮助弥合这一差距,引入了第一个可配置的物体探测解决方案,用一组重量来管理三重通信-计算-准确性交易。我们的解决方案显示了COCO-2017的最新成果,只增加了对基础高效D2结构的轻微处罚。我们的设计对基础架构和压缩机的选择非常健全,并且应该适应未来的架构。