6,078 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
Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks
Multilayered artificial neural networks (ANN) have found widespread utility
in classification and recognition applications. The scale and complexity of
such networks together with the inadequacies of general purpose computing
platforms have led to a significant interest in the development of efficient
hardware implementations. In this work, we focus on designing energy efficient
on-chip storage for the synaptic weights. In order to minimize the power
consumption of typical digital CMOS implementations of such large-scale
networks, the digital neurons could be operated reliably at scaled voltages by
reducing the clock frequency. On the contrary, the on-chip synaptic storage
designed using a conventional 6T SRAM is susceptible to bitcell failures at
reduced voltages. However, the intrinsic error resiliency of NNs to small
synaptic weight perturbations enables us to scale the operating voltage of the
6TSRAM. Our analysis on a widely used digit recognition dataset indicates that
the voltage can be scaled by 200mV from the nominal operating voltage (950mV)
for practically no loss (less than 0.5%) in accuracy (22nm predictive
technology). Scaling beyond that causes substantial performance degradation
owing to increased probability of failures in the MSBs of the synaptic weights.
We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the
sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due
to decoupled read and write paths. In an effort to further minimize the area
penalty, we present a synaptic-sensitivity driven hybrid memory architecture
consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation
framework shows that the proposed synaptic-sensitivity driven architecture
provides a 30.91% reduction in the memory access power with a 10.41% area
overhead, for less than 1% loss in the classification accuracy.Comment: Accepted in Design, Automation and Test in Europe 2016 conference
(DATE-2016
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural
networks (DNNs) proposed in the state of the art for image recognition. For
each DNN multiple performance indices are observed, such as recognition
accuracy, model complexity, computational complexity, memory usage, and
inference time. The behavior of such performance indices and some combinations
of them are analyzed and discussed. To measure the indices we experiment the
use of DNNs on two different computer architectures, a workstation equipped
with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson
TX1 board. This experimentation allows a direct comparison between DNNs running
on machines with very different computational capacity. This study is useful
for researchers to have a complete view of what solutions have been explored so
far and in which research directions are worth exploring in the future; and for
practitioners to select the DNN architecture(s) that better fit the resource
constraints of practical deployments and applications. To complete this work,
all the DNNs, as well as the software used for the analysis, are available
online.Comment: Will appear in IEEE Acces
- …