System-Theoretic Methods for Designing Bio-Inspired Mem-Computing Memristor Cellular Nonlinear Networks

The introduction of nano-memristors in electronics may allow to boost the performance of integrated circuits beyond the Moore era, especially in view of their extraordinary capability to process and store data in the very same physical volume. However, recurring to nonlinear system theory is absolutely necessary for the development of a systematic approach to memristive circuit design. In fact, the application of linear system-theoretic techniques is not suitable to explore thoroughly the rich dynamics of resistance switching memories, and designing circuits without a comprehensive picture of the nonlinear behaviour of these devices may lead to the realization of technical systems failing to operate as desired. Converting traditional circuits to memristive equivalents may require the adaptation of classical methods from nonlinear system theory. This paper extends the theory of time- and space-invariant standard cellular nonlinear networks with first-order processing elements for the case where a single non-volatile memristor is inserted in parallel to the capacitor in each cell. A novel nonlinear system-theoretic method allows to draw a comprehensive picture of the dynamical phenomena emerging in the memristive mem-computing array, beautifully illustrated in the so-called Primary Mosaic for the class of uncoupled memristor cellular nonlinear networks. Employing this new analysis tool it is possible to elucidate, with the support of illustrative examples, how to design variability-tolerant bio-inspired cellular nonlinear networks with second-order memristive cells for the execution of computing tasks or of memory operations. The capability of the class of memristor cellular nonlinear networks under focus to store and process information locally, without the need to insert additional memory units in each cell, may allow to increase considerably the spatial resolution of state-of-the-art purely CMOS sensor-processor arrays. This is of great appeal for edge computing applications, especially since the Internet-of-Things industry is currently calling for the realization of miniaturized, lightweight, low-power, and high-speed mem-computers with sensing capability on board.

Reduced speckle noise on medical ultrasound images using cellular neural network

Speckle noise is indispensable get from ultrasound image. In general tends to reduce the image resolution and contrast. In addition to the doctor diagnosis, is lacking for judgment accuracy. This paper is reduced the speckle noise and enhanced boundary of a tumor in the medical ultrasound images. The proposed method is valuated using numerical phantom simulating ultrasound B-mode images, and the effect is confirmed by applying to medical ultrasound images. Therefore, some important features such as tissue boundaries and small tumors may be overlooked. A cellular neural network which is a kind of recurrent neural network can deal with images by the weight of neurons called a cell. It could be obtained more detail images recognition compared with the previous studies. Determination template parameters of the cellular neural network for ultrasound image processing are discussed. The experimental results show effectiveness of applying the proposed method to boundary enhancement and the speckle noise reduction of medical ultrasound image.

A contour extraction method using active contour model on ultrasonic images

At excision operations of a breast tumor, a navigation to display three-dimensional models during the operations is demanded to grasp a position and a size of the tumor, and to decide an area of the excision. Speckle noises which are characteristic of an ultrasonic image are caused by interference of sound waves. The noises cause a low resolution of a region of interest (ROI), and those are obstacle of constructing recognizable three-dimensional image. To reconstruct a three-dimensional model from two-dimensional ultrasonic tomograms, a speckle reduction and a contour extraction of the ROI are demanded. The purpose of this study is a contour extraction of a ROI on ultrasonic images. An active contour model using a gradient vector flow was employed. The contour of a lesion area of the ultrasonic images which speckle are reduced was extracted.

Implementations of artificial neural networks using current-mode pulse width modulation technique

The use of a current-mode pulse width modulation (CM-PWM) technique to implement analog artificial neural networks (ANNs) is presented. This technique can be used to efficiently implement the weighted summation operation (WSO) that are required in the realization of a general ANN. The sigmoidal transformation is inherently performed by the nonlinear transconductance amplifier, which is a key component in the current integrator used in the realization of WSO. The CM-PWM implementation results in a minimum silicon area, and therefore is suitable for very large scale neural systems. Other pronounced features of the CM-PWM implementation are its easy programmability, electronically adjustable gains of neurons, and modular structures. In this paper, all the current-mode CMOS circuits (building blocks) required for the realization of CM-PWM ANNs are presented and simulated. Four modules for modular design of ANNs are introduced. Also, it is shown that the CM-PWM technique is an efficient method for implementing discrete-time cellular neural networks (DT-CNNs). Two application examples are given: a winner-take-all circuit and a connected component detector.

Optimal solutions for cellular neural networks by paralleled hardware annealing

An engineering annealing method for optimal solutions of cellular neural networks is presented. Cellular neural networks are very promising in solving many scientific problems in image processing, pattern recognition, and optimization by the use of stored program with predetermined templates. Hardware annealing, which is a paralleled version of mean-field annealing in analog networks, is a highly efficient method of finding optimal solutions of cellular neural networks. It does not require any stochastic procedure and henceforth can be very fast. The generalized energy function of the network is first increased by reducing the voltage gain of each neuron. Then, the hardware annealing searches for the globally minimum energy state by continuously increasing the gain of neurons. The process of global optimization by the proposed annealing can be described by the eigenvalue problems in the time-varying dynamic system. In typical nonoptimization problems, it also provides enough stimulation to frozen neurons caused by ill-conditioned initial states.

A board system for high-speed image analysis and neural networks

Two ANNA neural-network chips are integrated on a 6U VME board, to serve as a high-speed platform for a wide variety of algorithms used in neural-network applications as well as in image analysis. The system can implement neural networks of variable sizes and architectures, but can also be used for filtering and feature extraction tasks that are based on convolutions. The board contains a controller implemented with field programmable gate arrays (FPGA's), memory, and bus interfaces, all designed to support the high compute power of the ANNA chips. This new system is designed for maximum speed and is roughly 10 times faster than a previous board. The system has been tested for such tasks as text location, character recognition, and noise removal as well as for emulating cellular neural networks (CNN's). A sustained speed of up to two billion connections per second (GC/s) and a recognition speed of 1000 characters per second has been measured.