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==Related processing architectures==
CNN processors could be thought of as a hybrid between [[Artificial neural network|ANN]] and [[Continuous automaton|Continuous Automata]] (CA).
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==Model of computation==
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, inputs, 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 [[Multiscroll attractor|multiscroll chaos]] (like the [[Chen attractor]]),<ref>M. Yalcin, J. Suykens, and J. Vandewalle, Cellular Neural Networks, Multi-Scroll Chaos And Synchronization, 2005.</ref> [[Synchronization|synchronize]] with chaotic systems, and exhibit multi-level [[hysteresis]].<ref>A. Slavova and M. Markovat, "Receptor Based CNN Model with Hysteresis for Pattern Generation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>K. Yokosawa, Y. Tanji and M. Tanaka, "CNN with Multi-Level Hysteresis Quantization Output" Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>T. Nakaguchi, K. Omiya and M. Tanaka, "Hysteresis Cellular Neural Networks for Solving Combinatorial Optimization Problems", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> 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>
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* A research team from University degli Studi di Catania made one in order to generate gaits for a hexapod robot.
*
* Researchers from the [[National Lien Ho Institute of Technology|National Lien-Ho Institute of Technology]] (W. Yen, R. Chen and J. Lai) developed a Min-Max CNN (MMCNN) processor to learn more about CNN dynamics.<ref>W. Yen, R. Chen and J. Lai, "[https://ieeexplore.ieee.org/document/1035068 Design of Min/Max Cellular Neural Networks in CMOS Technology]" (IEEE), Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref>
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=== Holography, nanotechnology ===
Researchers are also perusing alternate technologies for CNN processors. Although current CNN processors circumvent some of the problems associated with their digital counterparts, they do share some of the same long-term problems common to all semiconductor-based processors. These include, but are not limited to, speed, reliability, power-consumption, etc. AnaLogic Computers, is developing optical CNN processors, which combine optics, lasers, and biological and [[Holography|holographic]] memories. What initially was technology exploration resulted in a 500x500 CNN processor able to perform 300 giga-operations per second.
Another promising technology for CNN processors is nanotechnology. One [[nanotechnology]] concept being investigated is using single electron tunneling junctions, which can be made into single-electron or high-current transistors, to create McCulloch-Pitts CNN processing units. In summary, CNN processors have been implemented and provide value to their users. They have been able to effectively leverage the advantages and address some of the disadvantages associated with their underling technology, i.e. semiconductors. Researchers are also transitioning CNN processors into emerging technologies. Therefore, if the CNN architecture is suited for a specific information processing system, there are processors available for purchase (as there will be for the foreseeable future).<ref>W. Porod, F. Werblin, L. Chua, T. Roska, A. Rodriguez-Vázquez, B. Roska, R. Faya, G. Bernstein, Y. Huang, and A. Csurgay, "Bio-Inspired Nano-Sensor-Enhanced CNN Visual Computer", Annals of the New York Academy of Sciences, 1013: 92–109, 2004.</ref> ==Applications==
CNN researchers have diverse interests, ranging from physical, engineering, theoretical, mathematical, computational, and philosophical applications.
=== Image processing ===
CNN processors were designed to perform image processing; specifically, real-time ultra-high frame-rate (>10,000 frame/s) processing for applications like particle detection in jet engine fluids and spark-plug detection. Currently, CNN processors can achieve up to 50,000 frames per second, and for certain applications such as missile tracking, flash detection, and spark-plug diagnostics these microprocessors have outperformed a conventional [[supercomputer]]. CNN processors lend themselves to local, low-level, processor intensive operations and have been used in feature extraction,<ref>O. Lahdenoja, M. Laiho and A. Paasio, "Local Binary Pattern Feature Vector Extraction with CNN", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> level and gain adjustments, color constancy detection,<ref>L. Torok and A. Zarandy, "CNN Based Color Constancy Algorithm", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> contrast enhancement, [[deconvolution]],<ref>L. Orzo, "Optimal CNN Templates for Deconvolution", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006</ref> [[image compression]],<ref>P. Venetianer and T. Roska, "Image Compression by Cellular Neural Networks," IEEE Trans. Circuits Syst., 45(3): 205-215, 1998.</ref><ref>R. Dogarut, R. Tetzlaffl and M. Glesner, "Semi-Totalistic CNN Genes for Compact Image Compression", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> motion estimation,<ref>Y. Cheng, J. Chung, C. Lin and S. Hsu, "Local Motion Estimation Based On Cellular Neural Network Technology for Image Stabilization Processing", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>A. Gacsadi, C. Grava, V. Tiponut, and P. Szolgay, "A CNN Implementation of the Horn & Schunck Motion Estimation Method", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> image encoding, image decoding, image segmentation,<ref>S. Chen, M. Kuo and J. Wang, "Image Segmentation Based on Consensus Voting", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>G. Grassi, E. Sciascio, A. Grieco and P. Vecchio, "A New Object-oriented Segmentation Algorithm based on CNNs - Part II: Performance Evaluation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> orientation preference maps,<ref>J. Wu, Z. Lin and C. Liou, "Formation and Variability of Orientation Preference Maps in Visual Cortex: an Approach Based on Normalized Gaussian Arrays", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref> pattern learning/recognition,<ref name=":2" /><ref>C. Wu and S. Tsai, "Autonomous Ratio-Memory Cellular Nonlinear Network (ARMCNN) for Pattern Learning and Recognition", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> multi-target tracking, image stabilization, resolution enhancement, image deformations and mapping, image inpainting, optical flow, contouring, [[moving object detection]],<ref>G. Costantini, D. Casali, and R. Perfetti, "Detection of Moving Objects in a Binocular Video Sequence", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> axis of symmetry detection,<ref>G Costantini, D. Casafi., and R. Perfetti, "A New CNN-based Method for Detection of the Axis of Symmetry.", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and [[image fusion]].<ref>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, "Image Processing Library for the Aladdin Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.</ref><ref>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, "Image processing library for the Aladdin Visual Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref><ref>K. Wiehler, M. Perezowsky, R. Grigat, "A Detailed Analysis of Different CNN Implementations for a Real-Time Image Processing System", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref>
Due to their processing capabilities and flexibility, CNN processors have been used and [[Prototype|prototyped]] for novel field applications such as flame analysis for monitoring combustion at a waste [[Incineration|incinerator]],<ref>L. Bertucco, A. Fichaa, G. Nmari and A. Pagano, "A Cellular Neural Networks Approach to Flame Image Analysis for Combustion Monitoring", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref> mine-detection using [[infrared]] imagery, [[calorimeter]] cluster peak for [[high energy physics]],<ref>C. Baldanza, F. Bisi, M. Bruschi, I. D’Antone, S. Meneghini, M. Riui, M. Zufa, "A Cellular Neural Network For Peak Finding In High-Energy Physics", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.</ref> anomaly detection in potential field maps for geophysics, laser dot detection, metal inspection for detecting manufacturing defects, and [[Seismology|seismic]] horizon picking. They have also been used to perform [[Biometrics|biometric]] functions<ref>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.</ref> such as [[fingerprint recognition]],<ref>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.</ref> vein feature extraction, face tracking,<ref>S. Xavier-de-Souza, M. Van Dyck, J. Suykens and J. Vandewalle, "Fast and Robust Face Tracking for CNN Chips: Application to Wheelchair Driving", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> and generating visual stimuli via emergent patterns to gauge perceptual [[Resonance|resonances]].
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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|nematodes]], 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>I. Gavrilut, V. Tiponut, and A. Gacsadi, "Path Planning of Mobile Robots by Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>P. Arena, P. Crucitti, L. Fortuna, M. Frasca, D. Lombardo and L. Patane, "Perceptive Patterns For Mobile Robots via RD-CNN and Reinforcement Learning", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>
=== Communication systems ===
The variety of dynamical behavior seen in CNN processors make them intriguing for communication systems. Chaotic communications using CNN processors is being researched due to their potential low power consumption, robustness and spread spectrum features. The premise behind chaotic communication is to use a chaotic signal for the carrier wave and to use chaotic phase synchronization to reconstruct the original message. CNN processors can be used on both the transmitter and receiver end to encode and decode a given message. They can also be used for data encryption and decryption, source authentication through watermarking,<ref>P. Arena, A. Basile, L. Fortuna, M. E. Yalcin, and J. Vandewalle, "Watermarking for the Authentication of Video on CNN-UM", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> detecting of complex patterns in spectrogram images<ref>K. Slot, P. Korbe, M. Gozdzik, and Hyongsuk Kim, "Pattern detection in spectrograms by means of Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> ([[sound processing]]), and transient spectral signals detection.
CNN processors are [[Neuromorphic engineering|neuromorphic]] processors, meaning that they emulate certain aspects of [[biological neural network]]s. The original CNN processors were based on mammalian retinas, which consist of a layer of [[Photodetector|photodetectors]] connected to several layers of locally coupled neurons. This makes CNN processors part of an interdisciplinary research area whose goal is to design systems that leverage knowledge and ideas from neuroscience and contribute back via real-world validation of theories. CNN processors have implemented a real-time system that replicates mammalian retinas, validating that the original CNN architecture chosen modeled the correct aspects of the biological neural networks used to perform the task in mammalian life. However, CNN processors are not limited to verifying biological neural networks associated with vision processing; they have been used to simulate dynamic activity seen in mammalian neural networks found in the olfactory bulb and locust [[antennal lobe]], responsible for pre-processing sensory information to detect differences in repeating patterns.<ref>C. Dominguez-Matas, R. Carmona-Galan, F. Sanchez-Fernaindez, J. Cuadri, and A. Rodriguez-Vaizquez, "A Bio-Inspired Vision Front-End Chip with Spatio-Temporal Processing and Adaptive Image Capture", Int’l Workshop on Computer Architecture for Machine Perception and Sensing, 2006.</ref><ref>A. Zarandy and C. Rekeczky, "Bi-i: a standalone ultra high speed cellular vision system", IEEE Circuits and Systems Magazine, 5(2):36-45, 2005.</ref>
CNN processors are being used to understand systems that can be modeled using simple, coupled units, such as living cells, biological networks, physiological systems, and ecosystems. The CNN architecture captures some of the dynamics often seen in nature and is simple enough to analyze and conduct experiments. They are also being used for [[stochastic]] simulation techniques, which allow scientists to explore spin problems, population dynamics, lattice-based gas models, [[percolation]], and other phenomena. Other simulation applications include heat transfer, mechanical vibrating systems, protein production,<ref>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.</ref> [[Josephson junction|Josephson Junction]] problems,<ref>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.</ref> seismic wave propagation, and geothermal structures. Instances of 3d CNN have been used to prove
CNN processors have been used to research a variety of mathematical concepts, such as
==Notes==
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*B. Shi, "Estimating the Steady State using Forward and Backward Recursions", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*S. Tokes, L. Orzo, and A. Ayoub, "Programmable OASLM as a Novel Sensing Cellular Computer", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*J. Flak, M. Laiho1, and K Halonen, "Programmable CNN Cell Based on SET Transistors", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*A. Zarandry, S. Espejo, P. Foldesy, L. Kek, G. Linan, C. Rekeczky, A. Rodriguez-Vazquez, T. Roska, I. Szatmari, T. Sziranyi and P. Szolgay, "CNN Technology in Action ", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
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*P. Ecimovic and J. Wu, "Delay Driven Contrast Enhancement using a Cellular Neural Network with State Dependent Delay", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*G. Cserey, C. Rekeczky and P. Foldesy, "PDE Based Histogram Modification with Embedded Morphological Processing of the Level Sets", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*H. Aomori, T. Otaket, N. Takahashi, and M. Tanaka, "A Spatial Domain Sigma Delta Modulator Using Discrete Time Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*H. Kim, H. Son. J. Lee, I. Kim and I. Kim, "An Analog Viterbi Decoder for PRML using Analog Parallel Processing Circuits of the CNN", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*S. Chen, M. Kuo and J. Wang, "Image Segmentation Based on Consensus Voting", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
*G. Timar and C. Rekeczky, "Multitarget Tracking Applications of the Bi-I Platform: Attention-selection, Tracking and Navigation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.
*Y. Cheng, J. Chung, C. Lin and S. Hsu, "Local Motion Estimation Based On Cellular Neural Network Technology for Image Stabilization Processing", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
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*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.
*I. Szatmhri, "The Implementation of a Nonlinear Wave Metric for Image Analysis and Classification on the 64x64 I/O CNN-UM Chip", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
*P. Arena, L. Fortuna, M. Frasca, and L. Patane, "CNN Based Central Pattern Generators with Sensory Feedback", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
*R. Caponetto, L. Fortuna, L. Occhipiniti, and M. G. Xibilia, "SC-CNN Chaotic Signals Generation", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*R. Chen and J. Lai, "Data Encryption Using Non-uniform 2-D Von Neumann Cellular Automata", Int’l Workshop on Cellular Neural Networks and Their Applications, 2005.
*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.
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*P. Lopez, D. Vilarino, D. Cabello, H. Sahli and M. Balsi, "CNN Based Thermal Modeling of the Soil for Anitpersonnel Mine Detection", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.
*P. Szolgay, T. Hidvegi, Z. Szolgay and P. Kozma, "A Comparison of the Different CNN Implementations in Solving the Problem of Spatiotemporal Dynamics in Mechanical Systems ", Int’l Workshop on Cellular Neural Networks and Their Applications, 2000.
*R. Brown and L. Chua, "Chaos or Turbulence", Int’l Journal of Bifurcation and Chaos, 2(4):1005-1009, 1992.
*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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