The normal SOM identifies input data of the same feature group by using all units of the map. We note that the number of different resources varies linearly according to the number of neurons on the output layer. Moreover, measure is often favored over measure d because it allows omitting the rooting operation and therefore decreases the computational complexity of the SOM algorithm [22, 23]. It has the capability of detecting novel data or clusters and creates new maps to learn this patterns avoiding that other receptive fields catastrophically … The NP modules have an innovative architecture compared to those proposed in the literature. Table 4 presents the experimental values compared to other studies that use maps of size almost similar to our SSOM network. Thus, all NPs forming SSOM will be placed in an array structure. In , Tamukoh and Sekine put forward a dynamical SOM hardware architecture. Moreover, unlike the architectures presented in the literature, our solution is clocked with a maximal frequency equal to 290 MHz whatever the SSOM topology. Indeed, this figure has two columns, and each one is specific to an SOM topology: 7 ∗ 7 and 16 ∗ 16. It is composed of three basic units. In , the authors presented an SOM-network implementation on an FPGA with a new asynchronous and parallel neighbourhood approach, based on the triangular neighbourhood function method. A project based in High Performance Computing. For the SSOM-architecture implementation, we will assign to each node of the connection grid an NP (Figure 6). Indeed, all these integration approaches have been generally widely used because they have the advantage of higher accuracy, better repeatability, lower noise sensitivity, better testability, and greater flexibility and compatibility with other types of neuroprocessors (NPs) constituting the neural network. Each neuron is linked to a referent vector, responsible for an area in the data space (also called the input space). Each NP is composed by two basic modules: the processing unit, called SOMPE, and the comparator unit with five inputs for the minimal distance extraction. In this part, we will adopt a systolic formalism based on the pipeline transmission of distances and identifiers between the neighbouring neurons. This architecture may well be used in an electroencephalogram (EEG) classification application already published in  but adopting a different architectural approach. The neurons in the input layer correspond to the input dimensions. In each column, we represent the obtained results: the palette of quantized colors of the original image (codebook) whose size depends on the topology of the Kohonen map used, the reconstructed image, and the values of MSE, PSNR, and CR. Flexible architecture of self organizing maps for changing environments Author SALAS, Rodrigo 1 2; ALLENDE, Héctor 2 3; MORENO, Sebastian 2; SAAVEDRA, Carolina 2  Universidad de Valparaíso; Departamento de Computación, Valparaíso, Chile  Universidad Técnica Federico Santa María; Dept. Several SOM implementations on FPGA supports have been proposed [11–18]. •Each computational node is connected to each input node to form a … The simplest approach is to label the trained map. 2019, Article ID 8212867, 14 pages, 2019. https://doi.org/10.1155/2019/8212867, 1University of Sousse, Higher Institute of Applied Sciences and Technology of Sousse, Sousse, Tunisia, 2University of Monastir, LR12ES06-Laboratory of Technology and Medical Imaging, Monastir, Tunisia. Almost all these parameters are specified during the design phase of the SOM. The SOM learning algorithm is competitive and runs in two steps: selecting the winning neuron and then updating the weights of the winning neuron and its neighbours. The direction of data exchange between nodes can perform the entire neural algorithm in its decision and learning phases. The latter will be performed concurrently in each neuroprocessor that will constitute the SOM network. The Self-Organizing Map was developed by professor Kohonen . This operation is executed until achieving a stabilized state at each node. 1. On the one hand, a high number of neurons in the input layer have a low impact on the variation in MCUPS. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. The size of this memory depends on the number of the elements of the weight vector and on the accuracy of each element in terms of bit number. Indeed, for instance in Figure 3, we differentiate nine processes of pipelined distance propagation between the various neuroprocessors (of coordinates ) in a systolic way. Each node provides the minimal squared distance as well as the identifier of the wining node over the SOM network. Each identifier, representing a part of the compressed image, will be saved. But the output layer remains an essential step for transforming data points into … Copyright © 2019 Khaled Ben Khalifa et al. The value of , which defines the variation in the maximal neighbourhood radius value as a function of the number of epochs, varies according to the value, as indicated by equation (10). The self-organizing map Abstract: The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. A massive parallel SOM neural network has been put forward. BibTex; Full citation; Publisher: Springer Berlin Heidelberg. Figure 4 illustrates the SOMPE architecture. Thus, a color palette is obtained by recovering the weights of neurons, called codebooks, at the end of the learning phase. The Architecture a Self Organizing Map We shall concentrate on the SOM system known as a Kohonen Network. For example, with a 16 × 16-SOM topology, the number of cycles corresponding to the decision and learning phases is equal to 65 and 99 clk, respectively. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. Thus, for any two neurons, and , if , then . A self-Organizing Map (SOM) varies from typical artificial neural networks (ANNs) both in its architecture and algorithmic properties. This model is formed by two levels of nested parallelism of neurons and connections. Moreover, scalability is used to provide data exchange mechanisms between neurons by interposing routing modules based on the Network-on-Chip (NoC) technique. The obtained results are provided in Section 6. The SSOM network is formed by a set of elementary NPs, each of which emulates the neuron operations. By keeping neighbourhood relationships in the grid, they allow an easy indexation formed by P ∗ Q neurons where P and Q are, respectively, the number of columns and rows (via coordinates in the grid). The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. Enter the email address you signed up with and we'll email you a reset link. For closer review of the applications published in the open literature, see section 2.3. Flexible Architecture of Self Organizing Maps for Changing Environments . represents the maximal radius that is initially equal to (P + Q). Self-organizing maps (SOMs) are a specific architecture of neural networks that cluster high-dimensional data vectors according to a similarity measure . As noted above, clustering the factor space allows to create a representative sample containing the training examples with the most unique sets of attributes for training an MLP. Its structure consists of a single layer linear 2D grid of neurons, rather than a … In an SOM, the reference vectors provide a discrete representation of the input space. In perspective, this same architecture could be adapted to a neural algorithm such as learning vector quantization (LVQ), which adopts the same training concept as the SOM with the only difference that it is supervised (it is mainly used for the classification). Most of these models have been executed in three phases: color quantification, compressed image generation, and image reconstruction. It provides a topology preserving mapping from the high dimensional space to map units. Self-Organizing Map PE Camera (3) Adaptation Extraction of salient regions Data propagation (1) Data aquisition Proprioception Fig. is the neighbourhood function already presented in Section 2 (equation (3)) and is already calculated by VEP. The ﬁrst layer leads the primitive signals to the preprocessing layer. Establishment of connections between SOM nodes (, Process scheduling during propagation phase (. Every self-organizing structure will have to … •2). In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. This article is organized as follows. The SSOM design has been developed using VHDL code and has been synthesized with Xilinx ISE Design Suite 14.4 tool. The solution suggested by the authors was structured around neural computation modules and a comparator whose resolution depended on the topology of an SOM network to be integrated. Noting that each of the is interconnected to all of these neighbours through bidirectional arcs that simultaneously broadcast and receive the minimum distances as well as the identifiers of the corresponding nodes (Figure 2(b)). 4 The Flexible Architecture of Self Organizing Maps The FASOM is a hybrid model that adapts K receptive fields of dynamical self organizing maps and learn the topology of partitioned spaces. The principle of compression and decompression is illustrated in Figure 10. algorithm for Artificial Neural Networks (A.N.N. A Self-Organizing Map (SOM) differs from typical ANNs both in its architecture and algorithmic properties. Multimodal FeedForward Self-organizing Maps 83 2 The Structure of the Feedforward Self-organizing Maps Neural networks have been inspired by the possibility of achieving information processing in ways that resemble those of biological neural systems. In this paper we introduce a hybrid algorithm called Flexible Architecture of Self Organizing Maps (FASOM) that overcomes the Catastrophic Interference and preserves the topology of Clustered data in changing environments. The SOM has been proven useful in many applications . The basic idea of the proposed architecture is to perform a neural computation in a competitive way at each node, as described in the introduction. The above shortcomings of both types of implementation devices may be avoided, thanks to reprogrammable circuits, such as field-programmable gate arrays (FPGA). The model consists in K receptive fields of self organizing maps. SOM’s architecture : Self organizin g maps have two layers, the first one is the input layer and the second one is the output layer or the feature map. The clusters are arranged in a low- dimensional topology – usually a grid structure – that preserves the neighbourhood relations existing in the high dimensional data. During the minimal distance extraction and the winning neuron identifier localization phases, scheduling processes are established. In , the same author put forward a new method to locate the winning neuron in an SOM network in one clock cycle. Obviously, each calculated distance in the SOM is positive d ≥ 0. Various hardware implementations of self-organizing map (SOM) neural networks on hardware circuits have been presented in the literature. in  proposed a massively parallel hardware solution for various neuron numbers on the SOM output map (16, 32, 64, 128, and 256 neurons). Note that represents the value of the minimal distance squared between the proper distance squared of the node in question and other delivered from the neighbours nodes: Thus, each node will propagate the distance and the identifier through a bus to the successor node. In , the authors have proposed a new scalable and adaptable SOM network hardware architecture. This project was built using CUDA (Compute Unified Device Architecture), C++ (C Plus Plus), C, CMake and JetBrains CLion. Accordingly, starting from an original image of size X ∗ Y pixels the binary size of the image after compression is equal to as follows: We notice that the size of the image depends on the resolution of P and Q and the number of pixels of the original image.  ,  . This palette will be used during the compression and decompression phases of the image. For this reason, both and lead to the same result in the process of identifying the winning neuron. Firstly, its structure comprises of a single-layer linear 2D grid of neurons, instead of a series of layers. In , Kurdthongmee put forward an approach to accelerate the learning phase of an SOM hardware architecture (called K-SOM) by evaluating the mean square error (MSE) after image color quantization. The architecture of each NP is composed of distance calculation modules and weight updates, classically defined in most bibliographic work. These circuits provide also low power consumption design. Indeed, the PSNR value varies according to the number of neurons on the output layer of the SOM network (PSNR rises as the number of neurons goes up). Variation in number of hardware resources (slice and LUT) for different SOM topologies with Dim. Variation in MCUPS depending on number of neurons on input layer with 7 × 7 and 16 × 16 neurons on the output layer. Most of these approaches depend on the SOM-architecture configuration, such as the number of input vector elements, output layer size, time constraints, and memory requirements. This solution enables us to reduce the time and the number of connections between the various SOM modules by eliminating the shared comparator and replacing it with local comparators for each neuroprocessor. Therefore, SOM forms a map where similar samples are mapped closely together. This is now the most used category of VLSI for neuromimetic algorithms. Indeed, the number of shifts is determined according to , which represents the neighbourhood radius between the neuron of coordinates and the winner neuron of coordinates (equation (7)). Each neuron is fully connected to all the source units in the input layer: The Self-Organizing Map is a common tool in RNNs. The output layer (map) contains as many neurons as … To validate our architecture, we use the color image (Figure 11). To integrate the SSOM network architecture, we propose to use NP modules where each emulates the operation of a neuron in SOM. The Architecture a Self Organizing Map We shall concentrate on the SOM system known as a Kohonen Network. So to switch from SOM to LVQ, it is simply a matter of modifying the SOMPE’ architecture (Figure 4) by removing the neighbourhood unit and adding an additional specific input to the expert’s label that is necessary for supervised learning. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author used a single 16 × 16 map to evaluate this approach on different images varying from 32 × 32 to 512 × 512 pixels. Each pixel will then be presented to the SSOM network to extract the identifier of the winning neuron “index”. The constructed architecture has been performed for XC7VX485t-2FFG1761 Xilinx FPGA family devices. They used the MANTRA I platform to validate their approaches. A one-dimensional map will just have a single row or column in the computational layer. The SOM algorithm is based on unsupervised, competitive learning. Therefore, instead of using the actual color code to encode a pixel, we use the position of the neuron having the closest weights (color) to the color of the observed pixel, hence reducing its size. Self-Organizing Maps for Cellular Hardware Architectures. The scenario of the project was a GPU-based implementation of the Self-Organising-Maps (S.O.M.) Each neuron is fully connected to all the source units in the input layer: This architecture, called systolic-SOM (SSOM), is formed by a set of the same nodes placed in a two-dimensional space. In Section 3, we highlight the problems solved by our approach. The suggested architecture was composed, in addition to the PE modules, of external control blocks and memory to save the weights of the neurons forming the SOM network. For this last point, three different technologies (60 nm, 40 nm, and 28 nm) specific to three types of FPGA supports of the Xilinx family were used. 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