11,565 research outputs found
Massively Parallel Video Networks
We introduce a class of causal video understanding models that aims to
improve efficiency of video processing by maximising throughput, minimising
latency, and reducing the number of clock cycles. Leveraging operation
pipelining and multi-rate clocks, these models perform a minimal amount of
computation (e.g. as few as four convolutional layers) for each frame per
timestep to produce an output. The models are still very deep, with dozens of
such operations being performed but in a pipelined fashion that enables
depth-parallel computation. We illustrate the proposed principles by applying
them to existing image architectures and analyse their behaviour on two video
tasks: action recognition and human keypoint localisation. The results show
that a significant degree of parallelism, and implicitly speedup, can be
achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi
Memcomputing: a computing paradigm to store and process information on the same physical platform
In present day technology, storing and processing of information occur on
physically distinct regions of space. Not only does this result in space
limitations; it also translates into unwanted delays in retrieving and
processing of relevant information. There is, however, a class of two-terminal
passive circuit elements with memory, memristive, memcapacitive and
meminductive systems -- collectively called memelements -- that perform both
information processing and storing of the initial, intermediate and final
computational data on the same physical platform. Importantly, the states of
these memelements adjust to input signals and provide analog capabilities
unavailable in standard circuit elements, resulting in adaptive circuitry, and
providing analog massively-parallel computation. All these features are
tantalizingly similar to those encountered in the biological realm, thus
offering new opportunities for biologically-inspired computation. Of particular
importance is the fact that these memelements emerge naturally in nanoscale
systems, and are therefore a consequence and a natural by-product of the
continued miniaturization of electronic devices. We will discuss the various
possibilities offered by memcomputing, discuss the criteria that need to be
satisfied to realize this paradigm, and provide an example showing the solution
of the shortest-path problem and demonstrate the healing property of the
solution path.Comment: The first part of this paper has been published in Nature Physics 9,
200-202 (2013). The second part has been expanded and is now included in
arXiv:1304.167
Assessing hyper parameter optimization and speedup for convolutional neural networks
The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures
Vision Science and Technology at NASA: Results of a Workshop
A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program
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