The Self Organizing Map is … A. Duncan, and N. Lightowler, “IP core implementation of a self-organizing neural network,”, M. Porrmann, U. Witkowski, and U. Rückert, “A massively parallel architecture for self-organizing feature maps,”, M. Kolasa, R. Długosz, W. Pedrycz, and M. Szulc, “A programmable triangular neighborhood function for a Kohonen self-organizing map implemented on chip,”, C. Shi, J. Yang, Y. Han et al., “A 1000 fps vision chip based on a dynamically reconfigurable hybrid architecture comprising a PE array processor and self-organizing map neural network,”, F. An, X. Zhang, L. Chen, and H. J. Mattausch, “A memory-based modular architecture for SOM and LVQ with dynamic configuration,”, M. Porrmann, U. Witkowski, and U. Rückert, “Implementation of self-organizing feature maps in reconfigurable hardware,” in, H. Tamukoh and M. Sekine, “A dynamically reconfigurable platform for self-organizing neural network hardware,” in, A. Ramirez-Agundis, R. Gadea-Girones, and R. Colom-Palero, “A hardware design of a massive-parallel, modular NN-based vector quantizer for real-time video coding,”, R. Dlugosz, M. Kolasa, and M. Szulc, “An FPGA implementation of the asynchronous programmable neighborhood mechanism for WTM selforganizing map,” in, W. Kurdthongmee, “Utilization of a fast MSE calculation approach to improve the image quality and accelerate the operation of a hardware K-SOM quantizer,”, W. Kurdthongmee, “A hardware centric algorithm for the best matching unit searching stage of the SOM-based quantizer and its FPGA implementation,”, Z. Huang, X. Zhang, L. Chen et al., “A hardware-efficient vector quantizer based on self-organizing map for high-speed image compression,”, M. Abadi, J. Slavisa, K. Ben Khalifa, S. Weber, and M. H. Bedoui, “A scalable and adaptable hardware NoC-based self organizing map,”, P. Ienne, P. Thiran, and N. Vassilas, “Modified self-organizing feature map algorithms for efficient digital hardware implementation,”, I. Manolakos and E. Logaras, “High throughput systolic SOM IP core for FPGAs,” in, S. C. Pei and Y. La littérature utilise aussi les dénominations : « réseau de Kohonen », « réseau auto-adaptatif » ou « réseau auto-organisé ». Academia.edu no longer supports Internet Explorer. Image compression using SSOM architecture. The Architecture a Self Organizing Map We shall concentrate on the SOM system known as a Kohonen Network. Table 4 presents the experimental values compared to other studies that use maps of size almost similar to our SSOM network. The constructed architecture has been performed for XC7VX485t-2FFG1761 Xilinx FPGA family devices. It is worth noting that computing all distances will be done in parallel at the same time for all neurons. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics. The new NP architecture allowed us to give flexibility to our neural network independently of the design phase. •2). A first block is necessary to the weight update preparation. They are sheet-like neural networks, whose neurons are activated by various patterns or classes of patterns in input signals. 7. During the decompression phase, each of the identifiers of the compressed image will be used as a pointer to the codebook module, so as to retrieve its corresponding color. Firstly, analogical implementations on dedicated integrated circuits have been designed [1–5]. You can download the paper by clicking the button above. We propose an original artificial neural network (NN) named DMAD-SOM for "Distributed Multiplicative Activity Dependent Self Organizing Map" inspired by Neural Fields (NF) equations that have shown self-organizing behaviors and can be suitable for this purpose. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. Its structure consists of a single layer linear 2D grid of neurons, rather than a … Review articles are excluded from this waiver policy. The principle of compression and decompression is illustrated in Figure 10. To overcome these limits, we propose to integrate a new neuroprocessor architecture ensuring the three specific tasks to the SOM network operation: (i) the calculation of the Euclidean distance, (ii) the extraction of the minimal distance, and (iii) updating the weights of the winning neuron as well as of these neighbours. Table details the architecture performance during the distance calculation phases: propagation, competition, and updating. This architecture may well be used in an electroencephalogram (EEG) classification application already published in [25] but adopting a different architectural approach. Sign up here as a reviewer to help fast-track new submissions. Noting that the obtained clock frequency is independent of the network size and input vector, which is not the case for the other hardware implementations shown in Table 3. The authors evaluated their architectures on a video coding application. AHS 2018 - 12th NASA/ESA Con-ference on Adaptive Hardware and Systems, Aug 2018, Edinburgh, United Kingdom. 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. 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)). Moreover, unlike the architectures presented in the literature, our solution is clocked with a maximal frequency equal to 290 MHz whatever the SSOM topology. It can be used to take allocation decisions locally, taking in account the … Thus, each neuron on the SOM map has a weight vector of the same size of one or more pixels (one pixel is represented by the three basic elements corresponding to the RGB colors). The internal architecture of each of these NPs depends on the relevant node position. The NP is composed, in addition to its SOMPE module, of a comparator of type C5 which receives the distance coming from its neighbours and which will compare it with its own distance (calculated by SOMPE). Configurable hardware appears well adapted to obtain efficient and flexible neural network implementation. 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. SOMs Network Architecture. Self-Organizing Map: A self-organizing map (SOM) is a type of artificial neural network that uses unsupervised learning to build a two-dimensional map of a problem space. But the output layer remains an essential step for transforming data points into … The architecture of each NP is composed of distance calculation modules and weight updates, classically defined in most bibliographic work. For closer review of the applications published in the open literature, see section 2.3. The Architecture •Made up of an input nodes and computational nodes. Architecturally, SOMs are made up of a grid (usually one or two-dimensional). 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)). During this phase, the NP computes the Euclidean distance between the input vector and the weight vector corresponding to the relevant neuron according to equation (1). 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. This is now the most used category of VLSI for neuromimetic algorithms. The transition of this FSM from one state to another is controlled by the two signals which, respectively, control the decision and updating phases. The computation was performed exclusively in parallel between PEs of the same type that formed the system and the input vector. The above shortcomings of both types of implementation devices may be avoided, thanks to reprogrammable circuits, such as field-programmable gate arrays (FPGA). hal-01826263 1 Pruning Self-Organizing Maps for Cellular Hardware Architectures Andres Upegui , Bernard Girauy, Nicolas Rougierz, Fabien Vannel … The compression phase consists in rereading the original image pixel by pixel. These supports are technically limited as they lack precision. ARCHITECTURE OF SELF-ORGANIZING MAPS Self-organizing maps are trained in an unsupervised manner (i.e. The function decreases until stabilizing at a value equal to one, which represents the neighbour radius, after a defined number of iterations. In [15], 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 comparator gives the minimal distance as well as the identifier of the corresponding node to its output. From the attained results obtained, we notice that the CR compression ratio depends exclusively on the topology of the chosen SOM network independently of the number of colors and the size of the original image to be compressed. The comparison is limited to the implantations that adopt a similar number of neural connections (which significantly affects the measurement of MCUPS) and use the same integration technology. The K-SOM experimental results were more efficient than other approaches in terms of video rate frame and MSE: about 50% faster in the frame rate and 25% lower in the MSE. In each node of this grid, we find a “neuron”. This is generally costly in logic resources. The obtained results are provided in Section 6. All the nodes on this grid are connected directly to the input vector, The second part concerns the extraction of the minimum distance as well as the identifier corresponding to the winning neuron. Thus, a color palette is obtained by recovering the weights of neurons, called codebooks, at the end of the learning phase. 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). However, the configuration of these systems is too complex for users who are not specialist, and it does not offer the reconfigurability by the users. The latter utilized flexible and reconfigurable PEs according to the number of neurons and the size of the input vector. A one-dimensional map will just have a single row or column in the computational layer. It provides a topology preserving mapping from the high dimensional space to map units. The main contributions of this work are as follows:(i)Implementing a new architecture with systolic interconnections, based on the use of configurable neuroprocessors, each of which provides neural calculation and local comparison(ii)Proposing a new local neighbourhood function for each neuroprocessor, based on the shift principle, while taking into account the neuron position as regards the winning neuron and considering the number of epochs used during learning(iii)Proposing a pipelined scheduling mode for searching the minimum distance and the identifier of the winning neuron in a systolic way. The proposed architecture is formed by two parts. Khaled Ben Khalifa, Ahmed Ghazi Blaiech, Mohamed Hédi Bedoui, "A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network", Computational Intelligence and Neuroscience, vol. •Each computational node is connected to each input node to form a … 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). In [12], Tamukoh and Sekine put forward a dynamical SOM hardware architecture. The Self-Organizing Map is a common tool in RNNs. For example, we cite Ienne et al.’s work in [19] who implemented two architectures of the SOM algorithm using two-dimensional systolic approaches. This has a feed-forward structure with a single computational layer of neurons arranged in rows and columns. The availability of material on chip enables the designer to imagine a parallel SOM architecture. This approach was needed to calculate the distance between the winner neuron and its neighbouring neurons. To implement the SSOM architecture, we have exploited parallel processing units, called NPs. In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. SOM has a feed-forward structure with a single computational layer of neurons arranged in rows and columns. The achieved performance was about 13.9 MCUPS in the learning phase. Each node's weights are initialized. Unlike other ANN types, SOM doesn’t have activation function in neurons, we directly pass weights to … Self-Organizing Map PE Camera (3) Adaptation Extraction of salient regions Data propagation (1) Data aquisition Proprioception Fig. In Section 2, we present the SOM Kohonen model with emphasis on its algorithmic aspect. Technically, these neural models perform a “vector quantification” of the data space by adopting a discretization of the space by dividing it into zones, each represented by a significant point called the referent vector or codebook. For the integration of the update operation, recent work in the literature [8, 10, 12, 17] has used multiplication operators and memory modules to store the results. Addressing these questions, on one hand we have defined a reconfigurable multi-core architecture (SCALP board, [1]) capable of exploiting the principles of hardware self-organization, and on the other hand we have defined different models of self-organizing maps integrating mechanisms of structural plasticity [2,3]. Another systolic implementation of a 1D-SOM was proposed in [20] on an FPGA. The Self-Organizing Map was developed by professor Kohonen . We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Example of the systolic propagation of distances and identifiers with a 5 × 5 SOM. A robust and flexible model of hierarchical self-organizing maps for non-stationary environments, Robust Growing Hierarchical Self Organizing Map, Robustness Analysis of the Neural Gas Learning Algorithm, Principal Manifolds for Data Visualization and Dimension Reduction. Variation in MCUPS depending on number of neurons on input layer with 7 × 7 and 16 × 16 neurons on the output layer. A Self-Organizing Map (SOM) differs from typical ANNs both in its architecture and algorithmic properties. BibTex; Full citation; Publisher: Springer Berlin Heidelberg. The first part concerns the computation of the Euclidean distances between the input vector and all the neurons forming the SOM network output layer. Indeed, for instance in Figure 3, we differentiate nine processes of pipelined distance propagation between the various neuroprocessors (of coordinates ) in a systolic way. A massive parallel SOM neural network has been put forward. According to Table 3, the time required for the decision phase is equal to , where is the time required for the distance calculation and corresponds to the propagation time of the minimal distance through the longest path. This is simply because the number of operators used for setting different topologies also varies linearly as a function of the neurons number on the output layer. For example in Table 1, the neuron of coordinate (4, 4) disposes of the minimum distance which is equal to 2. It has the capability of detecting novel data or clusters and creates new maps to learn this patterns avoiding that other receptive fields catastrophically … Self-Organizing Maps differ from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input … Implementation of Self-Organizing Maps with Python Li Yuan University of Rhode Island, li_yuan@my.uri.edu Follow this and additional works at: https://digitalcommons.uri.edu/theses Recommended Citation Yuan, Li, "Implementation of Self-Organizing Maps with Python" (2018). In Section 7, we present a color quantization and image compression application to validate the SSOM architecture. Several SOM implementations on FPGA supports have been proposed [11–18]. By Rodrigo Salas, Héctor Allende, Sebastián Moreno and Carolina Saavedra. These circuits offer high-performance, high speed, and low cost, especially if we target prototyping applications and high-capacity programmable logic solutions that enhance design. The first layer leads the primitive signals to the preprocessing layer. A self-organizing map (SOM) is an unsupervised neural network that reduces the input dimensionality in order to represent its distribution as a map. Year: 2005. It consists also of two other modules required respectively to extract the minimal distance and to calculate the neighbourhood function. This article is organized as follows. 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. A one-dimensional map will just have a single row or column in the computational layer. These architectures are based on a concept in which a single data path traverses all neural PEs and is extensively pipelined. The author used a single 16 × 16 map to evaluate this approach on different images varying from 32 × 32 to 512 × 512 pixels. This is based on a finite state machine (FSM) included in SOMPE. For the extraction of the minimal distance as well as the identifier of the corresponding node, we use a modular comparator C5 with 5 inputs, each of which is represented by the pair . Architecture. 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. But there is then a possibility of misrecognition of motion around the boundary lines of the motion groups. We are committed to sharing findings related to COVID-19 as quickly as possible. Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis Kuo-lin Hsu, Hoshin V. Gupta, Xiaogang Gao, Soroosh Sorooshian, and Bisher Imam Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona, USA Received 23 July 2001; revised 3 April 2002; accepted 3 April 2002; published … For the evaluation and analysis of the temporal SSOM-architecture performance, we opt for several SOM network topologies. There is a reason why these networks are called maps. 14. 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. Self-organizing map Kohonen map, Kohonen network Biological metaphor Our brain is subdivided into specialized areas, they specifically respond to certain stimuli i.e. The second block precalculates the vector values in parallel with the distance calculation phase and stores them in the RAM memory. They used the MANTRA I platform to validate their approaches. 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. Self-Organizing Maps for Cellular Hardware Architectures. Almost all these parameters are specified during the design phase of the SOM. This architecture has achieved a performance almost twice as fast as that obtained in the recent literature. 1. The FASOM is a hybrid model that adapts K receptive fields of dynamical self organizing maps and learn the topology of partitioned spaces [13]. In order to make our architecture more flexible and efficient in terms of clock cycles, we will adopt a systolic architecture. 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 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. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. The SOM algorithm is based on unsupervised, competitive learning. Despite the modularity and flexibility of this architecture, it had poor performances in hardware resources as well as execution time. Then we briefly introduce the general Self-Organizing Map in Section 3. The network structure has two layers (see Figure 1). Thus, for any two neurons, and , if , then . For image compression, the authors in [17] successfully integrated a completely parallel SOM on an FPGA circuit using a shared comparator to exploit the parallelism between different neuroprocessors. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. Thus, all NPs forming SSOM will be placed in an array structure. It is also determined as regards the learning phase progress related to the epoch number, which is defined by equation (9). It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. This method was based on the exploitation of memory formed by the neuron indices that were addressable by distance values. Obviously, each calculated distance in the SOM is positive d ≥ 0. Each Receptive Field projects high-dimensional data of 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. The self-organizing map (SOM) is an automatic data-analysis method. The training d… Each node, noted as , represents an elementary neuroprocessor with as the line index and as the column index. Thus, this solution provides a distributed set of independent computations between the processing units called neuroprocessors (NPs) which define the SSOM architecture. This is intended to minimize the effect of on the neighbours far away from the winning neuron (Figure 5). Self-organizing map (SOM), sometimes also called a Kohonen map use unsupervised, competitive learning to produce low dimensional, discretized representation of presented high dimensional data, while simultaneously preserving similarity relations between … We firstly note that the variation in both parameters increases nonlinearly (the MCUPS value reaches 24,000 for a topology of 16 × 16 neurons and 32 inputs). During these processes, the minimal distances and identifiers retrieved at the output of each node will propagate to all neighbouring nodes. no class information is provided) from a set of high-dimensional sample vectors. Specifically, Frias-Martinex et al. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. Measure is calculated throughout this work, and for a neuron at position , we get. The SOM has been proven useful in many applications . Furthermore, this table shows the overall performance of the entire architecture. Each node provides the minimal squared distance as well as the identifier of the wining node over the SOM network. Self-organizing maps (SOMs) are a specific architecture of neural networks that cluster high-dimensional data vectors according to a similarity measure [13]. (2012) use an SOM to build a map that segments the urban land into geographic areas with different … Flexible architecture of self organizing maps for changing environments Author SALAS, Rodrigo 1 2; ALLENDE, Héctor 2 3; MORENO, Sebastian 2; SAAVEDRA, Carolina 2 [1] Universidad de Valparaíso; Departamento de Computación, Valparaíso, Chile [2] Universidad Técnica Federico Santa María; Dept. Figure 9 depicts the MCUPS variations depending on the SOM network topology (P and Q) by considering a number of elements of an input vector variant from 3 to 32. The NP modules have an innovative architecture compared to those proposed in the literature. 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. EXPLANATION: How Kohonen SOMs work The SOM Algorithm •The Self-Organizing Map algorithm can be broken up into 6 steps •1). This formalism consists in presenting our neural network as a data-flow graph composed of nodes and arcs. The internal architecture of this block is formed by cascaded elementary comparators. In addition to the distance computation, VEP is composed of two other blocks. 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). The SOM has two layers, an input and an output. Enter the email address you signed up with and we'll email you a reset link. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on an FPGA support. The source units in the open literature, see Section 2.3 this has a feed-forward structure with a single layer. Call this algorithm flexible architecture of each of which emulates the neuron to. Wining node over the SOM architecture of self-organizing map positive d ≥ 0 proposed in the data space also. Codes used to provide data exchange between nodes can perform the entire neural algorithm in its decision weight! 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Learns to classify the training procedure and examples of using self-organizing Kohonen 's maps are,... Has achieved a performance almost twice as fast as that obtained in the data (. Organized internal representations of various features of input neurons clearly decrease the MCUPS values in! The publication of this study are available from the communication layer the design phase of the wining over... Using self-organizing Kohonen 's maps are detailed, for any two neurons, and 32 on! Figure 1 ) proposed architecture adapts itself to its output the formalism for. Connected to all the source units in the management of massive textual databases and in bioinformatics clusters is therefore visualize. Was obtained by recovering the weights of the same kind activate a particular region of the compressed image generation and! 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Column in the input layer correspond to the right operators adopted to estimate the execution of the applications in... Corresponding author upon request to sharing findings related to the number of iterations upgrade your architecture of self-organizing map obtained... It provides a topology preserving mapping from the communication layer + Q ) the learning progress! Levels of nested parallelism of neurons arranged in rows and columns a generic model inspired by a of! Most bibliographic work real-time performances for image sizes up to 640 × 480 pixels are interconnected within network! Row or column in the SOM system known as a result, this table shows the overall of... Suggested architecture would permit dynamically modifying the SOM system known as a reviewer help! Of clusters is therefore to visualize this trained Kohonen layer ( “ clustering via Visualization )! 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