6,206 research outputs found
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
Predictable migration and communication in the Quest-V multikernal
Quest-V is a system we have been developing from the ground up, with objectives focusing on safety, predictability and efficiency. It is designed to work on emerging multicore processors with hardware virtualization support. Quest-V is implemented as a ``distributed system on a chip'' and comprises multiple sandbox kernels. Sandbox kernels are isolated from one another in separate regions of physical memory, having access to a subset of processing cores and I/O devices. This partitioning prevents system failures in one sandbox affecting the operation of other sandboxes. Shared memory channels managed by system monitors enable inter-sandbox communication.
The distributed nature of Quest-V means each sandbox has a separate physical clock, with all event timings being managed by per-core local timers. Each sandbox is responsible for its own scheduling and I/O management, without requiring intervention of a hypervisor. In this paper, we formulate bounds on inter-sandbox communication in the absence of a global scheduler or global system clock. We also describe how address space migration between sandboxes can be guaranteed without violating service constraints. Experimental results on a working system show the conditions under which Quest-V performs real-time communication and migration.National Science Foundation (1117025
Development and Performance Evaluation of Network Function Virtualization Services in 5G Multi-Access Edge Computing
L'abstract è presente nell'allegato / the abstract is in the attachmen
5G-MEC Testbeds for V2X Applications
Fifth-generation (5G) mobile networks fulfill the demands of critical applications, such as Ultra-Reliable Low-Latency Communication (URLLC), particularly in the automotive industry. Vehicular communication requires low latency and high computational capabilities at the network’s edge. To meet these requirements, ETSI standardized Multi-access Edge Computing (MEC), which provides cloud computing capabilities and addresses the need for low latency. This paper presents a generalized overview for implementing a 5G-MEC testbed for Vehicle-to-Everything (V2X) applications, as well as the analysis of some important testbeds and state-of-the-art implementations based on their deployment scenario, 5G use cases, and open source accessibility. The complexity of using the testbeds is also discussed, and the challenges researchers may face while replicating and deploying them are highlighted. Finally, the paper summarizes the tools used to build the testbeds and addresses open issues related to implementing the testbeds.publishedVersio
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