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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： T. Roska Analogical and Neural Computing Laboratory of the Computer and Automation Research Institute of the Hungarian Academy of Science Budapest and the Department of Information Technology Pfizmhy P. Catholic University Budapest Hungary

A new principle of computing and computers is emerging: the analogic cellular computer. Its architecture, the Cellular neural Net (CNN) Universal Machine, is now implemented in several different physical forms and the first practical experiments exhibit very good frame rate and computing power. Several "Kilo real-time video" frame rate (more than 10000 frames per second) and TeraOPS computing power on a 1 cm/sup 2/ CMOS (0.5 micron) chip were measured. In this review article, the systems aspects and the new directions in this field are considered. A new world of software and a new notion of computing are taking ground. The possibility of software in optical computing becomes feasible. Likewise, programming on atomic and molecular scale implementations may be possible. Hence, photons and molecules may be used for signal representation in these analogic computers.

关键词： Cellular neural networks Sensor systems Optical computing computer architecture neural networks Turing machines Physics computing Power measurement Semiconductor device measurement Atomic measurements

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The computational infrastructure for cellular visual microprocessors

The computational infrastructure for cellular visual micropr...

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Proceedings of the International Conference on Microelectronics for neural, Fuzzy and Bio-Inspired systems, MicroNeuro

作者： P. Szolgay A. Zarandy S. Zold T. Roska P. Foldesy L. Kek T. Kozek K. Laszlo I. Petras Cs. Rekeczky I. Szatmari D. Balya Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A new computational paradigm is emerging for spatio-temporal problems: analogic CNN array-computing. The elementary instructions and programming techniques, however, are drastically different from any other computers. These elementary instructions represent complex, spatio-temporal, nonlinear, dynamic phenomena including all the standard and exotic properties of simple image processing operators as well as waves, patterns, and "evolving systems". Meanwhile, a computational infrastructure has also emerged interfacing this revolutionary computing technology to digital systems as well as enabling the development of high-level software. The main goal was to provide easy-to-use tools and hardware interfacing elements, and to put most of the sophistication in the chips and chip sets.

关键词： Microprocessors Cellular neural networks Prototypes computer aided instruction Image processing Digital systems Hardware Turing machines Embedded computing CMOS technology

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Face and Eye Detection by CNN Algorithms

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Journal of VLSI signal processing systems for signal, image and video technology 1999年第2期23卷 497-511页

作者： Balya, David Roska, Tamás Analogical and Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

A novel approach to critical parts of face detection problems is given, based on analogic cellular neural network (CNN) algorithms. The proposed CNN algorithms find and help to normalize human faces effectively while their time requirement is a fraction of the previously used methods. The algorithm starts with the detection of heads on color pictures using deviations in color and structure of the human face and that of the background. By normalizing the distance and position of the reference points, all faces should be transformed into the same size and position. For normalization, eyes serve as points of reference. Other CNN algorithm finds the eyes on any grayscale image by searching characteristic features of the eyes and eye sockets. Tests made on a standard database show that the algorithm works very fast and it is reliable.

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CNN Algorithms for Video Authentication and Copyright Protection

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Journal of VLSI signal processing systems for signal, image and video technology 1999年第2期23卷 449-463页

作者： Csapodi, Márton Vandewalle, Joos Preneel, Bart Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary ESAT SISTA/COSIC Laboratory Department of Electrical Engineering Katholieke Universiteit Leuven Leuven Belgium

This paper is concerned with the implementation of an image comparison algorithm and cryptographic hash functions on the regular array of cellular neural network (CNN) cells. These operations are necessary for message authentication and copyright protection in the multimedia service environment, serving both the interest of the copyright owner and the user. The basic problems of multimedia protection along with the requirements and role of the different actors in a multimedia service environment are also described here.

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Noise estimation and measures for detection of clustered microcalcifications 16th

Noise estimation and measures for detection of clustered mic...

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16th International conference on Information Processing in Medical Imaging, IPMI 1999

作者： Csapodi, Márton Petrányi, Ágota Liszka, György Zarándy, Ákos Roska, Tammás Analogic and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences P.O.B.63 BudapestH-1502 Hungary Department of Diagnostic Radiology National Institute of Oncology Kekgolyou. 13 BudapestH-1126 Hungary

ISBN: (纸本)3540661670

Image transforms used to preprocess mammogram images and highly selective microcalcification feature extraction are analyzed in this paper. It is demonstrated that the results obtained by the proposed method (especially at high true positive rates) exceed the specificity levels of other measures for which FROC analysis is provided using the same public (Nijmegen) database. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Mammography

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An Emulated Digital CNN Implementation

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Journal of VLSI signal processing systems for signal, image and video technology 1999年第2期23卷 291-303页

作者： Keresztes, Péter Zarándy, Ákos Roska, Tamás Szolgay, Péter Bezák, Tamás Hidvégi, Timót Jónás, Péter Katona, Attila István Széchenyi Polytechnic Győr Hungary Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary University of Veszprém Veszprém Hungary

A new emulated digital CNN Universal Machine chip architecture is introduced and the main steps of the design process are shown in this paper. One core processor can be implemented on 2 × 2 mm2 silicon area with a 0.35 μm CMOS technology. Assuming an array of 24 processors on a chip, its speed is 1ns/virtual cell/CNN iteration with 12 bit precision. This enables the execution of over five hundred 3 × 3 convolution operations on each frame of a 240 × 320-pixel 25 fps digital image flow. Another new feature of the design is its variable precision capability. This allows the user to trade off precision for speed. The architecture supports some non-linear filter implementation as well.

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A Nonlinear Wave Metric and its CNN Implementation for Object Classification

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Journal of VLSI signal processing systems for signal, image and video technology 1999年第2期23卷 437-447页

作者： Szatmári, István Rekeczky, Csaba Roska, Tamás Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary Electronics Research Laboratory College of Engineering University of California at Berkeley Berkeley USA

In this paper a nonlinear wave metric is introduced for object classification. It is shown that the choice of a metric is a nontrivial problem since it is easy to give examples when well-known distance measures, such as Hamming, Hausdorff, and Nonlinear Hausdorff metrics are completely inadequate for this classification. As an alternative a generalized theorem is proposed that includes the previous metrics as special cases. It is based on nonlinear wave propagation and defines a computational framework that is well-suited for parallel array processors. In this study we investigate different Cellular neural Network (CNN) architectures and solutions for the proposed metric and analyze its VLSI implementation complexity.

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An 0.5-/spl mu/m CMOS analog random access memory chip for TeraOPS speed multimedia video processing

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IEEE Transactions on Multimedia 1999年第2期1卷 121-135页

作者： R. Carmona-Galan A. Rodriguez-Vazquez S. Espejo-Meana R. Dominguez-Castro T. Roska T. Kozek L.O. Chua Instituto de Microelectronica de Sevilla Consejo Superior de Investigaciones Cientificas-Universidad de Sevilla Seville Spain MTA-SZTAKI Analogic and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Electronics Research Laboratory University of California Berkeley CA USA

Data compressing, data coding, and communications in object-oriented multimedia applications like telepresence, computer-aided medical diagnosis, or telesurgery require an enormous computing power-in the order of trillions of operations per second (TeraOPS). Compared with conventional digital technology, cellular neural/nonlinear network (CNN)-based computing is capable of realizing these TeraOPS-range image processing tasks in a cost-effective implementation. To exploit the computing power of the CNN Universal Machine (CNN-UM), the CNN chipset architecture has been developed-a mixed-signal hardware platform for CNN-based image processing. One of the nonstandard components of the chipset is the cache memory of the analog array processor, the analog random access memory (ARAM). This paper reports on an ARAM chip that has been designed and fabricated in a 0.5-/spl mu/m CMOS technology. This chip consists of a fully addressable array of 32/spl times/256 analog memory registers and has a packing density of 637 analog-memory-cells/mm/sup 2/. Random and nondestructive access of the memory contents is available. Bottom-plate sampling techniques have been employed to eliminate harmonic distortion introduced by signal-dependent feedthrough. Signal coupling and interaction have been minimized by proper layout measures, including the use of protection rings and separate power supplies for the analog and the digital circuitry. This prototype features an equivalent resolution of up to 7 bits-measured by comparing the reconstructed waveform with the original input signal. Measured access times for writing/reading to/from the memory registers are of 200 ns. I/O rates via the l6-line-wide I/O bus exceed 10 Msamples/s. Storage time at room temperature is in the 80 to 100 ms range, without accuracy loss.

关键词： Random access memory CMOS technology Image coding Image processing Cellular neural networks Registers Multimedia communication Multimedia computing Application software computer applications

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Invertible operations on a Cellular neural Network Universal Machine

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International Journal of Circuit Theory and Applications 1999年第6期26卷

作者： Márton Csapodi Joos Vandewalle Tamás Roska Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences P.O. Box 63 H-1502 Budapest Hungary ESAT Laboratory Department of Electrical Engineering Katholieke Universiteit Leuven K. Mercierlaan 94 B-3001 Leuven Belgium

This paper is concerned with the invertibility of parallel operations definable on two-dimensional binary arrays by means of a local Boolean function. In this way, it contributes to the theory of cellular automata and may open new potentialities to cellular neural network applications. We provide a theoretically new approach to the invertibility of two-dimensional additive maps which can be used to obtain detailed information on the behaviour of these operations. Other ways to make invertible operations are also investigated, including local maps which are additive in only one of the terms, and second-order cellular automata. The discussed techniques yield a rich variety of invertible operations on infinite and/or finite two-dimensional binary arrays. When implemented on CNN Universal Chips, all of these operations, performed by TeraOPS speed, become subroutines of a 2D analogic algorithm. These operations can be used in 2D cryptography. © 1998 John Wiley & Sons, Ltd.

关键词： CNN universal machine two-dimensional CA additive maps complementary landscapes

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CASTLE: an emulated digital CNN architecture design issues, new results

CASTLE: an emulated digital CNN architecture design issues, ...

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： A. Zarandy P. Keresztes T. Roska P. Szolgay Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A new emulated digital CNN universal machine chip architecture is introduced in this paper. The chip contains 24 CNN core processors using 70 mm/sup 2/ silicon area with a 0.35 /spl mu/m technology. Its speed is 1 ns/virtual cells/CNN iterations with 12 bit precision. This enables one to execute over five hundred 3/spl times/3 convolutions on each frame of a 240/spl times/320 sized 25 fps (standard NTSC) digital image flow. Another new feature of the design is the different precision capability. This allows the user to convert precision to speed.

关键词： Cellular neural networks computer architecture Optical feedback Equations Turing machines Silicon Convolution Image processing Very large scale integration Biomedical optical imaging

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