Cellular neural network: Difference between revisions

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==CNN architecture==

Due to their number and variety of [[Computer architecture|architectures]], it is difficult to give a precise definition for a CNN processor. From an architecture standpoint, CNN processors are a system of finite, fixed-number, fixed-___location, fixed-topology, locally interconnected, multiple-input, single-output, nonlinear processing units. The nonlinear processing units are often referred to as [[neurons]] or [[cell (biology)|cells]]. Mathematically, each cell can be modeled as a [[Dissipation|dissipative]], nonlinear [[dynamical system]] where information is encoded via its initial state, inputs and variables used to define its behavior. Dynamics are usually continuous, as in the case of [[Discrete time and continuous time|Continuous-Time]] CNN (CT-CNN) processors, but can be discrete, as in the case of [[Discrete time and continuous time|Discrete-Time]] CNN (DT-CNN) processors.<ref>S. Malki, Y. Fuqiang, and L. Spaanenburg, "Vein Feature Extraction Using DT-CNNs", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>H. Harrer and J.Nossek, "Discrete-Time Cellular Neural Networks", [[International Journal of Circuit Theory and Applications]], 20:453-467, 1992.</ref><ref>M. Brugge, "Morphological Design of Discrete−Time Cellular Neural Networks", University of Groningen Dissertation, 2005.</ref>

Each cell has one output, by which it communicates its state with both other cells and external devices. Output is typically [[Real number|real-valued]], but can be [[Complex number|complex]] or even [[quaternion]], i.e. a Multi-Valued CNN (MV-CNN). Most CNN processors, processing units are identical, but there are applications that require non-identical units, which are called Non-Uniform Processor CNN (NUP-CNN) processors, and consist of different types of cells.

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The idea of CNN processors was introduced by [[Leon Chua]] and Lin Yang in 1988.<ref>https://www.researchgate.net/publication/3183706_Cellular_neural_networks_Theory ("Cellular Neural Networks: Theory" and "Cellular Neural Networks: Applications" in IEEE Transactions on Circuits and Systems)</ref> In these articles, Chua and Yang outline the underlying mathematics behind CNN processors. They use this mathematical model to demonstrate, for a specific CNN implementation, that if the inputs are static, the processing units will converge, and can be used to perform useful calculations. They then suggest one of the first applications of CNN processors: image processing and pattern recognition (which is still the largest application to date). [[Leon O. Chua|Leon Chua]] is still active in CNN research and publishes many of his articles in the [[International Journal of Bifurcation and Chaos]], of which he is an editor. Both [[IEEE Circuits and Systems Society|IEEE Transactions on Circuits and Systems]] and the International Journal of Bifurcation also contain a variety of useful articles on CNN processors authored by other knowledgeable researchers. The former tends to focus on new CNN architectures and the latter more on the dynamical aspects of CNN processors.

In 1993, [[:nl:Tamás Roska|Tamas Roska]] and Leon Chua introduced the first algorithmically programmable analog CNN processor in the world.<ref name=":3">{{Cite journal|last1=Roska|first1=T.|last2=Chua|first2=L.O.|date=March 1993|title=The CNN universal machine: an analogic array computer|url=http://dx.doi.org/10.1109/82.222815|journal=IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing|volume=40|issue=3|pages=163–173|doi=10.1109/82.222815|issn=1057-7130|url-access=subscription}}</ref> The multi-national effort was funded by the [[Office of Naval Research]], the [[National Science Foundation]], and the [[Hungarian Academy of Sciences]], and researched by the Hungarian Academy of Sciences and the [[University of California, Berkeley|University of California]]. This article proved that CNN processors were producible and provided researchers a physical platform to test their CNN theories. After this article, companies started to invest into larger, more capable processors, based on the same basic architecture as the CNN Universal Processor. Tamas Roska is another key contributor to CNNs. His name is often associated with biologically inspired information processing platforms and algorithms, and he has published numerous key articles and has been involved with companies and research institutions developing CNN technology.

=== Literature ===

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=== Boolean functions ===

Like CA, computations can be performed through the generation and propagation of signals that either grow or change over time. [[Computation]]s can occur within a signal or can occur through the interaction between signals. One type of processing, which uses signals and is gaining momentum is [[Signal processing|wave processing]], which involves the generation, expanding, and eventual collision of waves. Wave processing can be used to measure distances and find optimal paths. Computations can also occur through particles, gliders, solutions, and filterons localized structures that maintain their shape and velocity.{{clarify|date=August 2023|what are these things? what is a filteron?}} Given how these structures interact/collide with each other and with static signals, they can be used to store information as states and implement different [[Boolean functions]]. Computations can also occur between complex, potentially growing or evolving localized behavior through worms, ladders, and pixel-snakes. In addition to storing states and performing [[Boolean function]]s, these structures can interact, create, and destroy static structures.<ref name=":1">{{Cite book|last1=Chen|first1=F.|last2=He|first2=G.|last3=Xu|first3=X.|last4=Chen|first4=G.|title=2006 10th International Workshop on Cellular Neural Networks and Their Applications |chapter=Implementation of Arbitrary Boolean Functions via CNN |date=August 2006|chapter-url=https://ieeexplore.ieee.org/document/4145881|pages=1–6|doi=10.1109/CNNA.2006.341641|isbn=1-4244-0639-0|s2cid=9648461}}</ref>

The applications of CNNs to Boolean functions is discussed in the paper by Fangyue Chen, Guolong He, Xiubin Xu, and [[Chen Guanrong|Guanrong Chen]], "Implementation of Arbitrary Boolean Functions via CNN".<ref name=":1" />

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Another company, AnaLogic Computers was founded in 2000 by many of the same researchers behind the first algorithmically programmable CNN Universal Processor. In 2003, AnaLogic Computers developed a PCI-X visual processor board that included the ACE 4K processor,<ref>{{Cite web|title=StackPath|url=https://www.vision-systems.com/home/article/16738443/cellular-device-processes-at-ultrafast-speeds|access-date=2020-12-27|website=www.vision-systems.com|date=February 2003 }}</ref> with a [[Texas Instruments|Texas Instrument]] DIP module and a high-speed frame-grabber. This allowed CNN processing to be easily included in a desktop computer. In 2006, AnaLogic Computers developed their Bi-I Ultra High Speed Smart Camera product line, which includes the ACE 4K processor in their high-end models.<ref>A. Rodríguez-Vázquez, G. Liñán-Cembrano, L. Carranza, E. Roca-Moreno, R. Carmona-Galán, F. Jiménez-Garrido, R. Domínguez-Castro, and S. Meana, "ACE16k: The Third Generation of Mixed-Signal SIMD-CNN ACE Chips Toward VSoCs," IEEE Trans. on Circuits and Systems - I, 51(5): 851-863, 2004.</ref>

In 2006, Roska et al. published a paper on designing a Bionic Eyeglass for AnaLogic. The Bionic Eyeglass is a dual-camera, wearable platform, based on the Bi-I Ultra High Speed Smart Camera, designed to provide assistance to blind people. Some of its functions include route number recognition and color processing.<ref>{{Cite book|last1=Roska|first1=T.|last2=Balya|first2=D.|last3=Lazar|first3=A.|last4=Karacs|first4=K.|last5=Wagner|first5=R.|last6=Szuhaj|first6=M.|title=2006 IEEE International Symposium on Circuits and Systems |chapter=System aspects of a bionic eyeglass |date=May 2006|chapter-url=https://ieeexplore.ieee.org/document/1692547|pages=4 pp.–164|doi=10.1109/ISCAS.2006.1692547|isbn=0-7803-9389-9|s2cid=3842486}}</ref><ref>K. Karacst and T. Roskatt, "Route Number Recognition of Public Transport Vehicles via the Bionic Eyeglass", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>R. Wagner and M. Szuhajt, "Color Processing in Wearable Bionic Glasses"</ref>

=== Analog CNN processors ===