2 research outputs found
Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications
Applying deep learning to object detection provides the capability to
accurately detect and classify complex objects in the real world. However,
currently, few mobile applications use deep learning because such technology is
computation-intensive and energy-consuming. This paper, to the best of our
knowledge, presents the first detailed experimental study of a mobile augmented
reality (AR) client's energy consumption and the detection latency of executing
Convolutional Neural Networks (CNN) based object detection, either locally on
the smartphone or remotely on an edge server. In order to accurately measure
the energy consumption on the smartphone and obtain the breakdown of energy
consumed by each phase of the object detection processing pipeline, we propose
a new measurement strategy. Our detailed measurements refine the energy
analysis of mobile AR clients and reveal several interesting perspectives
regarding the energy consumption of executing CNN-based object detection.
Furthermore, several insights and research opportunities are proposed based on
our experimental results. These findings from our experimental study will guide
the design of energy-efficient processing pipeline of CNN-based object
detection.Comment: This is a personal copy of the authors. Not for redistribution. The
final version of this paper was accepted by IEEE Transactions on Green
Communications and Networkin
Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles
As vehicles playing an increasingly important role in people's daily life,
requirements on safer and more comfortable driving experience have arisen.
Connected vehicles (CVs) can provide enabling technologies to realize these
requirements and have attracted widespread attentions from both academia and
industry. These requirements ask for a well-designed computing architecture to
support the Quality-of-Service (QoS) of CV applications. Computation offloading
techniques, such as cloud, edge, and fog computing, can help CVs process
computation-intensive and large-scale computing tasks. Additionally, different
cloud/edge/fog computing architectures are suitable for supporting different
types of CV applications with highly different QoS requirements, which
demonstrates the importance of the computing architecture design. However, most
of the existing surveys on cloud/edge/fog computing for CVs overlook the
computing architecture design, where they (i) only focus on one specific
computing architecture and (ii) lack discussions on benefits, research
challenges, and system requirements of different architectural alternatives. In
this paper, we provide a comprehensive survey on different architectural design
alternatives based on cloud/edge/fog computing for CVs. The contributions of
this paper are: (i) providing a comprehensive literature survey on existing
proposed architectural design alternatives based on cloud/edge/fog computing
for CVs, (ii) proposing a new classification of computing architectures based
on cloud/edge/fog computing for CVs: computation-aided and computation-enabled
architectures, (iii) presenting a holistic comparison among different
cloud/edge/fog computing architectures for CVs based on functional requirements
of CV systems, including advantages, disadvantages, and research challenges.Comment: This is a personal copy of the authors. Not for redistribution. The
final version of this paper is available through the IEEE Xplore Digital
Library, at the link: https://ieeexplore.ieee.org/document/9184917, with the
DOI: 10.1109/COMST.2020.302085