10,815 research outputs found
Cloud-based or On-device: An Empirical Study of Mobile Deep Inference
Modern mobile applications are benefiting significantly from the advancement
in deep learning, e.g., implementing real-time image recognition and
conversational system. Given a trained deep learning model, applications
usually need to perform a series of matrix operations based on the input data,
in order to infer possible output values. Because of computational complexity
and size constraints, these trained models are often hosted in the cloud. To
utilize these cloud-based models, mobile apps will have to send input data over
the network. While cloud-based deep learning can provide reasonable response
time for mobile apps, it restricts the use case scenarios, e.g. mobile apps
need to have network access. With mobile specific deep learning optimizations,
it is now possible to employ on-device inference. However, because mobile
hardware, such as GPU and memory size, can be very limited when compared to its
desktop counterpart, it is important to understand the feasibility of this new
on-device deep learning inference architecture. In this paper, we empirically
evaluate the inference performance of three Convolutional Neural Networks
(CNNs) using a benchmark Android application we developed. Our measurement and
analysis suggest that on-device inference can cost up to two orders of
magnitude greater response time and energy when compared to cloud-based
inference, and that loading model and computing probability are two performance
bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering
(IC2E) conference 201
Mobile Crowd Sensing in Edge Computing Environment
abstract: The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation.
This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Deep Learning in the Automotive Industry: Applications and Tools
Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.Comment: 10 page
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Internet of Things(IoT) devices, mobile phones, and robotic systems are often
denied the power of deep learning algorithms due to their limited computing
power. However, to provide time-critical services such as emergency response,
home assistance, surveillance, etc, these devices often need real-time analysis
of their camera data. This paper strives to offer a viable approach to
integrate high-performance deep learning-based computer vision algorithms with
low-resource and low-power devices by leveraging the computing power of the
cloud. By offloading the computation work to the cloud, no dedicated hardware
is needed to enable deep neural networks on existing low computing power
devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the
power of using cloud computing to perform real-time vision tasks. Furthermore,
to reduce latency and improve real-time performance, compression algorithms are
proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV
2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser
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