2 research outputs found
Collaborative Execution of Deep Neural Networks on Internet of Things Devices
With recent advancements in deep neural networks (DNNs), we are able to solve
traditionally challenging problems. Since DNNs are compute intensive,
consumers, to deploy a service, need to rely on expensive and scarce compute
resources in the cloud. This approach, in addition to its dependability on
high-quality network infrastructure and data centers, raises new privacy
concerns. These challenges may limit DNN-based applications, so many
researchers have tried optimize DNNs for local and in-edge execution. However,
inadequate power and computing resources of edge devices along with small
number of requests limits current optimizations applicability, such as batch
processing. In this paper, we propose an approach that utilizes aggregated
existing computing power of Internet of Things (IoT) devices surrounding an
environment by creating a collaborative network. In this approach, IoT devices
cooperate to conduct single-batch inferencing in real time. While exploiting
several new model-parallelism methods and their distribution characteristics,
our approach enhances the collaborative network by creating a balanced and
distributed processing pipeline. We have illustrated our work using many
Raspberry Pis with studying DNN models such as AlexNet, VGG16, Xception, and
C3D.Comment: Updated version after sysM
State-of-the-art Techniques in Deep Edge Intelligence
The potential held by the gargantuan volumes of data being generated across
networks worldwide has been truly unlocked by machine learning techniques and
more recently Deep Learning. The advantages offered by the latter have seen it
rapidly becoming a framework of choice for various applications. However, the
centralization of computational resources and the need for data aggregation
have long been limiting factors in the democratization of Deep Learning
applications. Edge Computing is an emerging paradigm that aims to utilize the
hitherto untapped processing resources available at the network periphery. Edge
Intelligence (EI) has quickly emerged as a powerful alternative to enable
learning using the concepts of Edge Computing. Deep Learning-based Edge
Intelligence or Deep Edge Intelligence (DEI) lies in this rapidly evolving
domain. In this article, we provide an overview of the major constraints in
operationalizing DEI. The major research avenues in DEI have been consolidated
under Federated Learning, Distributed Computation, Compression Schemes and
Conditional Computation. We also present some of the prevalent challenges and
highlight prospective research avenues.Comment: 13 pages, 5 figures, 1 tabl