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==Model of computation==
{{Section citations needed|date=December 2020}}
The dynamical behavior of CNN processors can be expressed using [[differential equations]], where each equation represents the state of an individual processing unit. The behavior of the entire CNN processor is defined by its initial conditions, the inputs, the cell interconnections (topology and weights), and the cells themselves. One possible use of CNN processors is to generate and respond to signals of specific dynamical properties. For example, CNN processors have been used to generate multi-scroll chaos, [[Synchronization|synchronize]] with chaotic systems, and exhibit multi-level hysteresis. CNN processors are designed specifically to solve local, low-level, processor intensive problems expressed as a function of space and time. For example, CNN processors can be used to implement high-pass and low-pass filters and [[Mathematical morphology|morphological]] operators. They can also be used to approximate a wide range of [[Partial differential equations]] (PDE) such as heat dissipation and wave propagation.<ref>P. Sonkolyt, P. Kozmat, Z. Nagyt and P. Szolgay, "Acoustic Wave Propagation Modeling on CNN-UM Architecture", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>
=== Reaction-Diffusion ===
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=== Digital CNN processors, FPGA ===
Although not nearly as fast and energy efficient, digital CNN processors do not share the problems of process variation and feature size of their analog counterparts. This allows digital CNN processors to include nested processor units, non-linearities, etc. In addition, digital CNN are more flexible, cost less and are easier to integrate. The most common implementation of digital CNN processors uses an [[Field-programmable gate array|FPGA]]. Eutecus, founded in 2002 and operating in Berkeley, provides intellectual property that can be synthesized into an Altera FPGA. Their digital 320x280, FPGA-based CNN processors run at 30 frame/s and there are plans to make a fast digital ASIC. Eustecus is a strategic partner of AnaLogic computers, and their FPGA designs can be found in several of AnaLogic’s products. Eutecus is also developing software libraries to perform tasks including but not limited to video analytics for the video security market, feature classification, multi-target tracking, signal and image processing and flow processing. Many of these routines are derived using CNN-like processing. For those wanting to perform CNN simulations for prototyping, low-speed applications, or research, there are several options. First, there are precise CNN emulation software packages like SCNN 2000. If the speed is prohibitive, there are mathematical techniques, such as Jacobi’s Iterative Method or Forward-Backward Recursions that can be used to derive the steady state solution of a CNN processor
=== Holography, nanotechnology ===
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*A. Chernihovskyi, C. Elger, and K. Lehnertz, "Effect of in Inhibitory Diffusive Coupling on Frequency-Selectivity of Excitable Media Simulated With Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*R. Carmona, F. Jimenez-Garrido, R. Dominguez-Castro, S. Espejo and A. Rodriguez-Vazquez, "CMOS Realization of a 2-layer CNN Universal Machine", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*D. Balya and B. Roska, "A Handy Retina Exploration Device", Workshop on Cellular Neural Networks and Their Applications, 2005.
*P. Arena, M. Bediat, L. Fortuna, D. Lombardo, L. Patane, and M. Velardet, "Spatio-temporal Patterns in CNNs for Classification: the Winnerless Competition Principle", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
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*W. Samarrai, J. Yeol, I. Bajis and Y. Ryu, "System Biology Modeling of Protein Process using Deterministic Finite Automata (DFA)", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
*V. Mladenovt, and A. Slavoval, "On the Period Solutions in One Dimensional Cellular Neural Networks based on Josephson Junctions", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*R. Brown and L. Chua, "Chaos or Turbulence", Int’l Journal of Bifurcation and Chaos, 2(4):1005-1009, 1992.
*E. Gunay, M. Alci and S. Parmaksizoglu, "N-Scroll Generation in SC-CNN via Neuro Fuzzy Based Non-Linear Function", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*M. Gilli, F. Corinto, and P. Checco, "Periodic Oscillations and Bifurcations in Cellular Nonlinear Networks", IEEE Trans. on Circuits and Systems – I, 51(5):948-962, 2004.
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