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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|
=== 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. 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 journal|
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" />
=== Automata and Turing machines ===
Although CNN processors are primarily intended for analog calculations, certain types of CNN processors can implement any Boolean function, allowing simulating CA. Since some CA are [[Universal Turing machine]]s (UTM), capable of [[Simulation|simulating]] any algorithm can be performed on processors based on the [[von Neumann architecture]], that makes this type of CNN processors, universal CNN, a UTM. One CNN architecture consists of an additional layer. CNN processors have resulted in the simplest realization of [[Conway’s Game of Life]] and [[Rule 110|Wolfram’s Rule 110]], the simplest known universal [[Turing machine|Turing Machine]]. This unique, dynamical representation of an old systems, allows researchers to apply techniques and hardware developed for CNN to better understand important CA. Furthermore, the continuous state space of CNN processors, with slight modifications that have no equivalent in [[Cellular Automata]], creates [[Emergence|emergent]] behavior never seen before.<ref name=":0">{{Cite journal|
Any information processing platform that allows the construction of arbitrary [[Boolean function]]s is called universal, and as result, this class CNN processors are commonly referred to as universal CNN processors. The original CNN processors can only perform linearly separable Boolean functions. By translating functions from digital logic or look-up table domains into the CNN ___domain, some functions can be considerably simplified. For example, the nine-bit, odd parity generation logic, which is typically implemented by eight nested exclusive-or gates, can also be represented by a sum function and four nested absolute value functions. Not only is there a reduction in the function complexity, but the CNN implementation parameters can be represented in the continuous, real-number ___domain.<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}}</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 journal|
=== Analog CNN processors ===
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There is an ongoing effort to incorporate CNN processors into sensory-computing-actuating machines as part of the emerging field of [[Cellular Machines]]. The basic premise is to create an integrated system that uses CNN processors for the sensory signal-processing and potentially the decision-making and control. The reason is that CNN processors can provide a low power, small size, and eventually low-cost computing and actuating system suited for Cellular Machines. These Cellular Machines will eventually create a Sensor-Actuator Network (SAN),<ref>M. Haenggi, "Mobile Sensor-Actuator Networks: Opportunities and Challenges", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> a type of Mobile Ad Hoc Networks (MANET) which can be used for military intelligence gathering, surveillance of inhospitable environments, maintenance of large areas, planetary exploration, etc.
CNN processors have been proven versatile enough for some control functions. They have been used to optimize function via a genetic algorithm,<ref>D. Balya and V. Galt, "Analogic Implementation of the Genetic Algorithm", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> to measure distances, to perform optimal path-finding in a complex, dynamic environment, and theoretically can be used to learn and associate complex stimuli. They have also been used to create antonymous gaits and low-level motors for robotic [[nematode]]s, spiders, and lamprey gaits using a Central Pattern Generator (CPG). They were able to function using only feedback from the environment, allowing for a robust, flexible, biologically inspired robot motor system. CNN-based systems were able to operate in different environments and still function if some of the processing units are disabled.<ref>{{cite arxiv|
=== Communication systems ===
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