  Videos take a lot of time to transport over the network, hence running
analytics on the live video on embedded or mobile devices has become an
important system driver. Considering that such devices, e.g., surveillance
cameras or AR/VR gadgets, are resource constrained, creating lightweight deep
neural networks (DNNs) for embedded devices is crucial. None of the current
approximation techniques for object classification DNNs can adapt to changing
runtime conditions, e.g., changes in resource availability on the device, the
content characteristics, or requirements from the user. In this paper, we
introduce ApproxNet, a video object classification system for embedded or
mobile clients. It enables novel dynamic approximation techniques to achieve
desired inference latency and accuracy trade-off under changing runtime
conditions. It achieves this by enabling two approximation knobs within a
single DNN model, rather than creating and maintaining an ensemble of models
(e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at
runtime to these changes, provides low and stable latency for the image and
video frame classification problems, and show the improvement in accuracy and
latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17],
NestDNN [MobiCom-18], and MSDNet [ICLR-18].
