Add to ORKGhttp://lib.tkk.fi/Books/2001/isbn9512263378/papers/1191.pdfIn this paper we introduce a novel simulation time bounded behavioral modeling technique that optimally selects the incorporated block models. The method has been specially developed for fast performance evaluation of large mixed signal image processing arrays. The time domain accuracy is optimized under the simulation time constraint by automatic selection of various user supplied block models. A dedicated environment also has been developed for efficient numerical simulations. Utilizing the proposed methodology, a bridge has been built for the CNN-UM VLSI implementations between the device level and the high-level functionality
A graphical environment for CNN algorithm development is presented. The new generation of graphical ...
Due to their local connectivity and wide functional capabilities, cellular nonlinear networks (CNN) ...
Abstract — A fast timing analysis of plane circuits via two-layer CNN-based modeling, which is neces...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The state of the art work in cellular neural networks (CNN) has concentrated on VLSI implementations...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The state of the art work in Cellular Neural Networks (CNN) has concentrated on VLSI implementations...
This paper presents SIRENA, a CAD environment for the simulation and modeling of mixed-signal VLSI p...
This paper presents a feasibility analysis to predict the optimal size of VLSI CNN implementations. ...
An efficient behavioral simulator for Cellular Neural Networks (CNN) is presented in this paper. The...
Due to the character of the original source materials and the nature of batch digitization, quality ...
This paper presents an analysis of the stability and convergence properties of the Full Signal Range...
Abstract. This paper describes a full-custom mixed-signal chip that embeds digitally programmable an...
This paper presents SIRENA, a CAD environment for the simulation and modelling of mixed-signal VLSI ...
A practical system approach for time-multiplexing cellular neural network (CNN) implementations suit...
A graphical environment for CNN algorithm development is presented. The new generation of graphical ...
Due to their local connectivity and wide functional capabilities, cellular nonlinear networks (CNN) ...
Abstract — A fast timing analysis of plane circuits via two-layer CNN-based modeling, which is neces...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The state of the art work in cellular neural networks (CNN) has concentrated on VLSI implementations...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
The state of the art work in Cellular Neural Networks (CNN) has concentrated on VLSI implementations...
This paper presents SIRENA, a CAD environment for the simulation and modeling of mixed-signal VLSI p...
This paper presents a feasibility analysis to predict the optimal size of VLSI CNN implementations. ...
An efficient behavioral simulator for Cellular Neural Networks (CNN) is presented in this paper. The...
Due to the character of the original source materials and the nature of batch digitization, quality ...
This paper presents an analysis of the stability and convergence properties of the Full Signal Range...
Abstract. This paper describes a full-custom mixed-signal chip that embeds digitally programmable an...
This paper presents SIRENA, a CAD environment for the simulation and modelling of mixed-signal VLSI ...
A practical system approach for time-multiplexing cellular neural network (CNN) implementations suit...
A graphical environment for CNN algorithm development is presented. The new generation of graphical ...
Due to their local connectivity and wide functional capabilities, cellular nonlinear networks (CNN) ...
Abstract — A fast timing analysis of plane circuits via two-layer CNN-based modeling, which is neces...