8,988 research outputs found
A binary self-organizing map and its FPGA implementation
A binary Self Organizing Map (SOM) has been designed and
implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of the binary features
Binary object recognition system on FPGA with bSOM
Tri-state Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, has been used for classification rather than clustering in this paper. The major contribution here is the demonstration of the potential use of the modified bSOM in security surveillance, as a recognition system on FPGA
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Among the many possible approaches for the parallelization of self-organizing
networks, and in particular of growing self-organizing networks, perhaps the
most common one is producing an optimized, parallel implementation of the
standard sequential algorithms reported in the literature. In this paper we
explore an alternative approach, based on a new algorithm variant specifically
designed to match the features of the large-scale, fine-grained parallelism of
GPUs, in which multiple input signals are processed at once. Comparative tests
have been performed, using both parallel and sequential implementations of the
new algorithm variant, in particular for a growing self-organizing network that
reconstructs surfaces from point clouds. The experimental results show that
this approach allows harnessing in a more effective way the intrinsic
parallelism that the self-organizing networks algorithms seem intuitively to
suggest, obtaining better performances even with networks of smaller size.Comment: 17 page
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
Mining Dynamic Document Spaces with Massively Parallel Embedded Processors
Currently Océ investigates future document management services. One of these services is accessing dynamic document spaces, i.e. improving the access to document spaces which are frequently updated (like newsgroups). This process is rather computational intensive. This paper describes the research conducted on software development for massively parallel processors. A prototype has been built which processes streams of information from specified newsgroups and transforms them into personal information maps. Although this technology does speed up the training part compared to a general purpose processor implementation, however, its real benefits emerges with larger problem dimensions because of the scalable approach. It is recommended to improve on quality of the map as well as on visualisation and to better profile the performance of the other parts of the pipeline, i.e. feature extraction and visualisation
Hybrid quantum-classical unsupervised data clustering based on the Self-Organizing Feature Map
Unsupervised machine learning is one of the main techniques employed in
artificial intelligence. Quantum computers offer opportunities to speed up such
machine learning techniques. Here, we introduce an algorithm for quantum
assisted unsupervised data clustering using the self-organizing feature map, a
type of artificial neural network. We make a proof-of-concept realization of
one of the central components on the IBM Q Experience and show that it allows
us to reduce the number of calculations in a number of clusters. We compare the
results with the classical algorithm on a toy example of unsupervised text
clustering
FPGA-Based Acceleration of the Self-Organizing Map (SOM) Algorithm using High-Level Synthesis
One of the fastest growing and the most demanding areas of computer science is Machine Learning (ML). Self-Organizing Map (SOM), categorized as unsupervised ML, is a popular data-mining algorithm widely used in Artificial Neural Network (ANN) for mapping high dimensional data into low dimensional feature maps. SOM, being computationally intensive, requires high computational time and power when dealing with large datasets. Acceleration of many computationally intensive algorithms can be achieved using Field-Programmable Gate Arrays (FPGAs) but it requires extensive hardware knowledge and longer development time when employing traditional Hardware Description Language (HDL) based design methodology. Open Computing Language (OpenCL) is a standard framework for writing parallel computing programs that execute on heterogeneous computing systems. Intel FPGA Software Development Kit for OpenCL (IFSO) is a High-Level Synthesis (HLS) tool that provides a more efficient alternative to HDL-based design. This research presents an optimized OpenCL implementation of SOM algorithm on Stratix V and Arria 10 FPGAs using IFSO. Compared to recent SOM implementations on Central Processing Unit (CPU) and Graphics Processing Unit (GPU), our OpenCL implementation on FPGAs provides superior speed performance and power consumption results. Stratix V achieves speedup of 1.41x - 16.55x compared to AMD and Intel CPU and 2.18x compared to Nvidia GPU whereas Arria 10 achieves speedup of 1.63x - 19.15x compared to AMD and Intel CPU and 2.52x compared to Nvidia GPU. In terms of power consumption, Stratix V is 35.53x and 42.53x whereas Arria 10 is 15.82x and 15.93x more power efficient compared to CPU and GPU respectively
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
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