  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.
