ApproxNet: Content and Contention Aware Video Analytics System for the
Edge
release_wvgvtawlvrchdakzrmn3hnjm2i
by
Ran Xu, Jinkyu Koo, Rakesh Kumar, Peter Bai, Subrata Mitra, Ganga
Meghanath, Saurabh Bagchi
2019
Abstract
Videos take lot of time to transport over the network, hence running
analytics on live video at the edge devices, right where it was captured has
become an important system driver. However these edge devices, e.g., IoT
devices, surveillance cameras, AR/VR gadgets are resource constrained. This
makes it impossible to run state-of-the-art heavy Deep Neural Networks (DNNs)
on them and yet provide low and stable latency under various circumstances,
such as, changes in the resource availability on the device, the content
characteristics, or requirements from the user. In this paper we introduce
ApproxNet, a video analytics system for the edge. It enables novel dynamic
approximation techniques to achieve desired inference latency and accuracy
trade-off under different system conditions and resource contentions,
variations in the complexity of the video contents and user requirements. It
achieves this by enabling two approximation knobs within a single DNN model,
rather than creating and maintaining an ensemble of models (such as in MCDNN
[Mobisys-16]). Ensemble models run into memory issues on the lightweight
devices and incur large switching penalties among the models in response to
runtime changes. We show that ApproxNet can adapt seamlessly at runtime to
video content changes and changes in system dynamics to provide low and stable
latency for object detection on a video stream. We compare the accuracy and the
latency to ResNet [2015], MCDNN, and MobileNets [Google-2017].
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