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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>
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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In the 2000s, AnaFocus, a mixed-signal semiconductor company from the the [[University of Seville]], introduced their ACE prototype CNN processor product line. Their first ACE processor contained 20x20 B/W processor units; and subsequent processors provided 48x48 and 128x128 grayscale processor units, improving the speed and processing elements. AnaFocus also had a multilayer CASE prototype CNN processors line. Their processors allowed real-time interaction between the sensing and processing. In 2014, AnaFocus had been sold to e2v technologies.<ref>{{Cite web|title=Acquisition of AnaFocus: Fast-growing CMOS imaging business to be integrated into High Performance Imaging division|url=https://www.teledyne-e2v.com/news/acquisition-of-anafocus-fast-growing-cmos-imaging-business-to-be-integrated-into-high-performance-imaging-division/|access-date=2020-12-27|website=e2v}}</ref>
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}}</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>
=== Analog CNN processors ===
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One potential future application of CNN microprocessors is to combine them with DNA microarrays to allow for a near-real time DNA analysis of hundreds of thousands of different DNA sequences. Currently, the major bottleneck of DNA [[microarray analysis]] is the amount of time needed to process data in the form of images, and using a CNN microprocessor, researchers have reduced the amount of time needed to perform this calculation to 7ms.
=== Texture recognition ===
CNN processors have also been used to generate and analyze patterns and textures. One motivation was to use CNN processors to understand pattern generation in natural systems. They were used to generate [[Turing pattern]]s in order to understand the situations in which they form, the different types of patterns which can emerge, and the presence of defects or asymmetries.<ref name=":0" /> Also, CNN processors were used to approximate pattern generation systems that create stationary fronts, [[spatio-temporal pattern]]s [[Oscillation|oscillating]] in time, [[hysteresis]], memory, and heterogeneity. Furthermore, pattern generation was used to aid high-performance image generation and compression via real-time generation of [[stochastic]] and coarse-grained biological patterns, texture boundary detection, and pattern and [[texture recognition]] and classification.<ref>E. David, P. Ungureanu, and L. Goras, "On he Feature Extraction Performances of Gabor-Type Filters in Texture Recognition Applications", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>C. Lin and S. Chen, "Biological Visual Processing for Hybrid-Order Texture Boundary Detection with CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>
==Control and Actuator Systems==
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*T. Roska, L. Chua, "The CNN Universal Machine: An Analogic Array Computer", IEEE Trans. on Circuits and Systems-II, 40(3): 163-172, 1993.
*T. Roska and A. Rodriguez-Vazquez, "Review of CMOS Implementations of the CNN Universal Machine-Type Visual Microprocessors", International Symposium on Circuits and Systems, 2000
*T. Roska, "Cellular Wave Computers and CNN Technology – a SoC architecture with xK Processors and Sensor Arrays", Int’l Conference on Computer Aided Design Accepted Paper, 2005.
*K. Karahaliloglu, P. Gans, N. Schemm, and S. Balkir, "Optical sensor integrated CNN for Real-time Computational Applications", IEEE Int’l Symposium on Circuits and Systems, pp. 21–24, 2006.
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*K. Huang, C. Chang, W. Hsieh, S. Hsieh, L. Wang and F. Tsai, "Cellular Neural Network For Seismic Horizon Picking", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
*T. Su, Y. Du, Y. Cheng, and Y. Su, "A Fingerprint Recognition System Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*R. Dogaru and I. Dogaru, "Biometric Authentication Based on Perceptual Resonance Between CNN Emergent Patterns and Humans", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*Q. Feng, S. Yu and H. Wang, "An New Automatic Nucleated Cell Counting Method With Improved Cellular Neural Networks (ICNN)", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
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*C. Lin and S. Chen, "Biological Visual Processing for Hybrid-Order Texture Boundary Detection with CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*G. Costantini, D. Casali, and M. Carota, "A Pattern Classification Method Based on a Space-Variant CNN Template", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*T. Roska and L. O. Chua, "The CNN Universal Machine: 10 Years Later, Journal of Circuits, Systems, and Computers", Int’l Journal of Bifurcation and Chaos, 12(4):377-388, 2003.
*R. Bise, N. Takahashi and T. Nishi, "On the Design Method of Cellular Neural Networks for Associate Memories Based on Generalized Eigenvalue Problem", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
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