7,696 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
Distributed learning of CNNs on heterogeneous CPU/GPU architectures
Convolutional Neural Networks (CNNs) have shown to be powerful classification
tools in tasks that range from check reading to medical diagnosis, reaching
close to human perception, and in some cases surpassing it. However, the
problems to solve are becoming larger and more complex, which translates to
larger CNNs, leading to longer training times that not even the adoption of
Graphics Processing Units (GPUs) could keep up to. This problem is partially
solved by using more processing units and distributed training methods that are
offered by several frameworks dedicated to neural network training. However,
these techniques do not take full advantage of the possible parallelization
offered by CNNs and the cooperative use of heterogeneous devices with different
processing capabilities, clock speeds, memory size, among others. This paper
presents a new method for the parallel training of CNNs that can be considered
as a particular instantiation of model parallelism, where only the
convolutional layer is distributed. In fact, the convolutions processed during
training (forward and backward propagation included) represent from -\%
of global processing time. The paper analyzes the influence of network size,
bandwidth, batch size, number of devices, including their processing
capabilities, and other parameters. Results show that this technique is capable
of diminishing the training time without affecting the classification
performance for both CPUs and GPUs. For the CIFAR-10 dataset, using a CNN with
two convolutional layers, and and kernels, respectively, best
speedups achieve using four CPUs and with three GPUs.
Modern imaging datasets, larger and more complex than CIFAR-10 will certainly
require more than -\% of processing time calculating convolutions, and
speedups will tend to increase accordingly
Adaptive transfer functions: improved multiresolution visualization of medical models
The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-016-1253-9Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to 512x512x2000 voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctorsâ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.Peer ReviewedPostprint (author's final draft
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