{"title":"Realization of CNNs by optical parallel processing with spatial light valves","authors":"N. Fruhauf, E. Luder","doi":"10.1109/CNNA.1990.207533","DOIUrl":"https://doi.org/10.1109/CNNA.1990.207533","url":null,"abstract":"Proposes an optical realization of cellular neural networks (CNNs). Any implementation of CNNs must calculate correlations of templates and input or output states of neurons. Therefore the system described is based on an optical special purpose processor which is perfectly suited for the computation of real time correlations. The processor is made of lenses and electronically addressed liquid crystal light valves which permit real time modifications of inputs and templates. Large neural nets and templates which are not restricted in their connectivity can be realized with massive optical parallel processing.<<ETX>>","PeriodicalId":142909,"journal":{"name":"IEEE International Workshop on Cellular Neural Networks and their Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115572280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A new type of hierarchical neural-like networks: representation","authors":"P. Martín-Smith, A. Prieto, F. Pelayo, J. Ortega","doi":"10.1109/CNNA.1990.207535","DOIUrl":"https://doi.org/10.1109/CNNA.1990.207535","url":null,"abstract":"Presents a new kind of hierarchical binary network which may be considered as a specific case of cellular networks. Such networks are defined by primitives or processes which are personalised for network implementation in specific applications. The way in which a network may be described is provided by a process (network process) which may be used both for simulation software and for deducing the general hardware architecture of the network. Following a description of the algorithms of different simulation primitives, a network example for noise elimination in images is included to illustrate the potential of this kind of network.<<ETX>>","PeriodicalId":142909,"journal":{"name":"IEEE International Workshop on Cellular Neural Networks and their Applications","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133345867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Rodriquez-Vazques, R. Domínguez-Castro, J. Huertas
{"title":"Accurate design of analog CNN in CMOS digital technologies","authors":"A. Rodriquez-Vazques, R. Domínguez-Castro, J. Huertas","doi":"10.1109/CNNA.1990.207532","DOIUrl":"https://doi.org/10.1109/CNNA.1990.207532","url":null,"abstract":"Explores the design of cellular neural networks (CNN) by using sampled-data analog current-mode techniques which neither requires capacitors nor resistors but just MOS transistors. The feature makes the proposed technique well suited for implementation in conventional VLSI MOS technologies. A set of building blocks is presented and their performance validated by device-level simulation results. Also, guidelines are given concerning the choice of the circuit parameters for optimum operation.<<ETX>>","PeriodicalId":142909,"journal":{"name":"IEEE International Workshop on Cellular Neural Networks and their Applications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114738737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"pRAM automata","authors":"T.G. Clarkson, D. Gorse, J.G. Taylor","doi":"10.1109/CNNA.1990.207528","DOIUrl":"https://doi.org/10.1109/CNNA.1990.207528","url":null,"abstract":"The probabilistic random access memory (pRAM) is a device with neuron-like behaviour. It differs from the conventional model of a neuron in that it may implement nonlinear functions and is stochastic in operation. A features of this device is that it is hardware-realisable and VLSI devices have been made of a four-input version, the 4-pRAM. The basic pRAM device can have a learning function added to it and one such learning task is presented. pRAM arrays have applications in computer imaging and their incorporation into a modular image processing system is described.<<ETX>>","PeriodicalId":142909,"journal":{"name":"IEEE International Workshop on Cellular Neural Networks and their Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116936712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}