{"title":"Integration of sensor/processor under cellular neural networks paradigm for multimedia applications","authors":"B. Sheu, K. Cho, W. C. Young","doi":"10.1109/CNNA.1998.685327","DOIUrl":null,"url":null,"abstract":"Compact, high computing power systems become feasible with significant progress in the research and development of advanced computing architecture and array processing. A scalable image sensor array processor with frame memory buffer and cellular neural network (CNN) for nearest neighbor interaction has been developed in a 0.5 mm HP CMOS technology. A CNN with analog programmable weights was constructed with compact mixed-signal circuit components in the current-mode technique. The low voltage, low power operation is supported with the current mode scheme which scales appropriately with the supply voltage. Design of a variable gain neuron circuit can be incorporated into the prototype to realize the optimal solution capability using hardware annealing.","PeriodicalId":171485,"journal":{"name":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1998.685327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Compact, high computing power systems become feasible with significant progress in the research and development of advanced computing architecture and array processing. A scalable image sensor array processor with frame memory buffer and cellular neural network (CNN) for nearest neighbor interaction has been developed in a 0.5 mm HP CMOS technology. A CNN with analog programmable weights was constructed with compact mixed-signal circuit components in the current-mode technique. The low voltage, low power operation is supported with the current mode scheme which scales appropriately with the supply voltage. Design of a variable gain neuron circuit can be incorporated into the prototype to realize the optimal solution capability using hardware annealing.
随着先进计算体系结构和阵列处理技术的研究和发展取得重大进展,紧凑、高计算能力的系统成为可能。采用0.5 mm HP CMOS技术开发了一种可扩展的图像传感器阵列处理器,该处理器具有帧存储缓冲和用于最近邻交互的细胞神经网络(CNN)。采用电流模技术,利用紧凑的混合信号电路元件构建了具有模拟可编程权重的CNN。电流模式方案可根据电源电压适当缩放,支持低电压、低功耗运行。可在原型中加入可变增益神经元电路的设计,利用硬件退火实现最优解能力。