Novel analogic algorithm for finding the shortest-path using simple CNN structure

Balkir

2003

Circuit Theory & Apps

SUMMARYThe processing capabilities of a proposed nanoelectronic device are investigated. The device is considered as a global dynamical system with local circuit model components. The system equations and the corresponding network model are presented. The characteristics of this network model are compared with the cellular neural networks. Certain characteristics of the network are analysed theoretically and demonstrated with circuit-system level simulations. As a novel property, it is shown that the single layer nanodevice network structure is a basic reaction-di usion system and it is capable of autowave propagation. Furthermore, the same network structure exhibits several image processing capabilities like image smoothing, edge enhancement, and horizontal or vertical line detection using simple arrangements of the device parameters.

Section: Autowaves In Rtnmentioning

confidence: 99%

Nanostructure array of coupled RTDs as cellular neural networks

Balkir

2003

Circuit Theory & Apps

“…One such example is the realization of reaction-diffusion systems with autonomous CNNs [1][2][3]. Several applications of reaction-diffusion CNN (RD-CNN) include artificial locomotion [2], image processing [4,5], shortest path solution [6] and pattern recognition [7]. Derivation of neuron models described by biological mechanisms is yet another systematic approach that can lead to efficient CNN hardware implementations [8].…”

Section: Introductionmentioning

confidence: 99%

Compact MOS Implementation of a Reaction-Diffusion CNN

Analog Integr Circ Sig Process

Balkir

2005

A biologically inspired single layer cellular neural network (CNN) with trigger wave formation capability is presented. A novel compact MOS cell circuit is proposed which exhibits a third order I-V characteristic with negative differential resistance (NDR). Certain D.C. characteristics of both the proposed cell and the network are described and corresponding theoretical estimations are presented. It is shown that the CNN formed by resistive coupling of these cells has very low complexity and realizes a reaction-diffusion system. The dynamical network behavior is demonstrated by transient simulations of a 2D cell array at the circuit level.

“…The frequently highlighted applications of such systems II. THE SYSTEM MODEL include artificial locomotion [4], [5] and path optimization The dynamical system model is a resistive-coupled 2D array [6]. A vast CNN application area regards image processing, with well-known and implemented examples such as [7]- [9].…”

mentioning

confidence: 99%

Optical Sensor Integrated CNN for Real-Time Computational Applications

2006 IEEE International Symposium on Circuits and Systems

Gans²,

Schemm³

et al.

A previously implemented CNN layer based on Nagumo neuroelectric model [11]. Hence, the cell states are a bio-inspired system model is integrated with optical sensors excitable, which results in trigger wave propagation [12]. comprised of PN type photodiodes and the design is fabricated Implementation examples of FitzHugh-Nagumo models exist in CMOS technology. The implemented test chip is measured and obtained results are presented. The sensor integration marks a agan as OTA desilgns, such as in [13]. It was shown however, new step in prototyping of this system in CMOS, which allows that an essential part of similar functionality can be realized the cell states to be programmed via light interactions, rather using custom design approach [14], which will significantly than successive state programming procedure which was utilized improve the on-chip network size.previously. It is shown that the cell input states in the network In this aper we address a CMOS integration of our can be successfully introduced via exposure of the 2D array to p p g a light source. The followed custom design methods and this custom CNN design with PN type photodiodes. The result is remarkably compatible sensor integration can lead to compact a CNN compactly equipped with pixel sensors, which offers on-chip solutions for real-time high performance applications, great potential in computational applications as a stand-alone including but not limited to certain types of image processing. system or a building block. For this purpose, another test chip in 1.5 micron CMOS technology is implemented, which I. INTRODUCTION primarily validates the functionality of the sensor integrated The neuromorphic systems in nature are ultimate references design. Built upon this step, system adaptations to different for improving system models and their design methods. The applications will be rather straightforward, with peripheral cellular nonlinear (or neural) network (CNN) theory can modifications in terms of control circuitry which addresses extensively address such models. There are previous examples the particular computational task. Along with these efforts, of this parallelism in the literature, with their implementations. this sensor integrated CNN design will remarkably boost the A particular category deals with spatiotemporally excitable real time computational performance for specific applications. models, which are implementable via autonomous CNNs [1]-[3]. The frequently highlighted applications of such systems II. THE SYSTEM MODEL include artificial locomotion [4], [5] and path optimization The dynamical system model is a resistive-coupled 2D array [6]. A vast CNN application area regards image processing, with well-known and implemented examples such as [7]-[9]. of nonlinear cells which corresponds to a specific single layer withwelommownapproachorlelted systmpls dsign is to em-CNN as depicted in Fig. 1. The dynamical state equationThe common approach for relatedl systems dlesign iS to emotie rmtecretrlto o ahntoknd ''i ploy top-down oper...