Conventional Cellular Neural Networks Research Articles

Multilayer RTD-memristor-based cellular neural networks for color image processing

Multilayer cellular neural networks (CNNs) with multiple state variables in each cell associated with multiple dynamic rules are believed to possess more powerful data computing and signal processing capabilities than single-layer CNNs and are specially suitable for solving complex problems. However, at present, their large scale integrated hardware implementation is still quite challenging based on traditional CMOS-based technology due to high circuity complexity and their applications are thus limited in practice. In this paper, a novel compact multilayer CNN model based on nanometer scale resonant tunneling diodes (RTDs) and memristors is presented. More specifically, in this model, one multilayer CNN cell consists of several sub-cells located in different layers. The resonant tunneling diode with quantum tunneling induced nonlinearity and uniquely folded current–voltage characteristics is used to implement the compact and high-speed cell via replacing the original linear resistor and removing the output function of conventional CNN cells. Furthermore, the interactions between these cells are determined by a pair of multi-dimensional cloning templates. And a compact synaptic circuit based on memristors is designed to realize the cloning template parameter (weight strength) and the multiplication (weighting) operation, by leveraging its nonvolatility, good scalability, and variable conductance. The combination of these desirable elements equips the proposed multilayer CNN with advantages of powerful processing capability as well as high compactness, versatility, and possibility of very large scale integration (VLSI) circuit implementations. Finally, the performance of the proposed multilayer CNN is validated by five illustrative examples in color image processing with each layer dealing with each primary color (red, green or blue) plane.

Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing

Cellular neural network (CNN) has been acted as a high-speed parallel analog signal processor gradually. However, recently, since the decrease in the size of transistor is going to approach the utmost, the transistor-based integrated circuit technology hits a bottleneck. As a result, the advantage of very large scale integration implementation of CNN becomes hard to really present, and further development of this era faces severe challenges unavoidably. In this study, two types of memristor-based cellular neural networks have been proposed. One type uses a memristor to replace the linear resistor in a conventional CNN cell circuit. And the other places a resonant tunneling diode (RTD) in this position and uses memristive synaptic connections to structure a hybrid memristor RTD CNN model. The excellent performances of the proposed CNNs are verified by conventional means of, for instance, stability analysis and efficient applications in image processing. Since both the memristor and the resonant tunneling diode are nanoscale, the size of the network circuits can be greatly reduced, and the integration density of the system will be significantly improved.

A CNN-Specific Integrated Processor

Integrated Processors (IP) are algorithm-specific cores that either by programming or by configuration can be re-used within many microelectronic systems. This paper looks at Cellular Neural Networks (CNN) to become realized as IP. First current digital implementations are reviewed, and the memoryprocessor bandwidth issues are analyzed. Then a generic view is taken on the structure of the network, and a new intra-communication protocol based on rotating wheels is proposed. It is shown that this provides for guaranteed high-performance with a minimal network interface. The resulting node is small and supports multi-level CNNdesigns, giving the systema 30-fold increase in capacity compared to classical designs. As it facilitates multiple operations on a single image, and single operations on multiple images, with minimal access to the external image memory, balancing the internal and external data transfer requirements optimizes the system operation. In conventional digital CNN designs, the treatment of boundary nodes requires additional logic to handle the CNN value propagation scheme. In the new architecture, only a slight modification of the existing cells is necessary to model the boundary effect. A typical prototype for visual pattern recognition will house 4096 CNN cells with a 2% overhead for making it an IP.

Cellular Neural Networks With Transient Chaos

A new model of cellular neural networks (CNNs) with transient chaos is proposed by adding negative self-feedbacks into CNNs after transforming the dynamic equation to discrete time via Euler's method. The simulation on the single neuron model shows stable fix points, bifurcation and chaos. Hence, this new CNN model has richer and more flexible dynamics, and therefore may possess better capabilities of solving various problems, compared to the conventional CNN with only stable dynamics

A CMOS Implementation of a 1-Bit Multi-Cell Encoded—Cellular Neural Network

A new Cellular Neural Network (CNN)-based system has been implemented and tested to demonstrate the ability of this novel system to process large digital images more rapidly than its conventional CNN counterpart. The multi-cell encoded CNN processes the data of multiple single-data CNN cells within each multi-cell encoded cell enabling the new architecture it's advantages in loading, processing, unloading speed and in layout. A one bit (1B) 4-cell-encoded CNN was implemented to illustrate this new system. In this CMOS implementation, data of four neighboring conventional 1B CNN cells are encoded for processing within encoded cells. The CMOS circuits and circuit networks of one encoded cell are presented. Due to area limitations, each test chip includes the hardware for only one one-dimensional encoded cell. Experimental results of one-chip and two-chip 1B, multi-cell encoded systems are presented for connected-component detection and edge detection test cases. These results demonstrate the correct response of this implementation due to variations in template values, boundary values, and initial conditions. Interactions between encoded cell components and encoded cells are demonstrated for both dynamic and static responses. This CMOS implementation validates this new CNN architecture and provides a template for implementations using more advanced device technologies and circuits.

Cellular Neural Networks for Associative Memory with Multiple Memory Tables

It is reported that the CNNs (Cellular Neural Networks) have a high ability of associative memory. However, the number of memory patterns and their similarity influence the recall capability of conventional CNNs. Hence, it is considered that the appropriate number of memory patterns which gives the maximum of the recall capability exists. In this paper, we investigate the influence of memory patterns on the recall capability in CNNs, and estimate the range of the appropriate number of memory patterns.Furthermore, we propose the CNN for associative memory with Multiple Memory Tables (MTT-CNN) in order to apply to practical fields. Thereupon, in order to verify the usefulness of the MMT-CNN, we performed the experiments by using 6×6, 12×12, 15×15 MMT-CNN, and examined the incomplete recall rate in each experiment. As a result, the incomplete recall rate of the MTT-CNN was far below that of the conventional CNN, and we could confirm an improvement of self-recall function. Moreover, we could avoid the incomplete recall completely by choosing more appropriate divisions.

A mixed‐mode polynomial‐type CNN for analysing brain electrical activity in epilepsy

AbstractIn this paper, a mixed‐mode polynomial‐type cellular neural network (CNN) for analysing brain electrical activity in epilepsy is presented. The principles and design characteristics of this application are overviewed. The main difference of the requirements of this application compared to conventional CNN realizations is that in addition to linear, cells are also coupled with polynomial‐type couplings. A mixed‐mode CNN is shown to be suitable for the realization of a polynomial‐type CNN in the brain electrical activity analysis application. In a mixed‐mode CNN multiplication is done in analog domain, whereas the integration and storage are digital. Suitability of different integration methods for cell level realization are studied and cell and network level design of a mixed‐mode CNN is described. First‐, second‐ and third‐order polynomial feedback terms are included in the cell and Heun's integration method is used. In order to reduce the cell count, the array is designed so that it can process input data that has been divided into blocks. The whole input data is stored in parallel with the cells so that all input/output operations during processing are local. Cell structure is shown along with register connections between edge cells of the network. Analog power consumption and computing speed are estimated by HSPICE simulations using 0.25 µm digital CMOS process parameters. The die area of a network with 2×72 cells with 36 layers in each was determined by drawing the layout. Copyright © 2002 John Wiley & Sons, Ltd.