Abstract. The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Universal Machine (CNN-UM) as a Cellular Wave Computer, gives new perspectives for com-putational physics. Many numerical problems and simulations can be elegantly addressed on this fully parallelized and analogic architecture. Here we study the possibility of performing stochastic simulations on this chip. First a realistic random number generator is implemented on the CNN-UM, and then as an example the two-dimensional Ising model is studied by Monte Carlo type simulations. The results obtained on an experimental version of the CNN-UM with 128 × 128 cells are in good agreement with the results obtained on digital computers. Computationa...
This paper describes the design of a programmable Cellular Neural Network (CNN) chip, with additiona...
[eng] Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized ...
Cellular Neural Networks (CNN's) represent a remarkable improvement in hardware implementation of Ar...
The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Unive...
The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Unive...
Abstract. This paper describes a full-custom mixed-signal chip that embeds digitally programmable an...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
This paper present the application of Pulse Stream Tech-niques (PSTs) to the hardware implementation...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
In recent years, many applications have been implemented in embedded systems and mobile Internet of ...
This paper describes a full-custom mixed-signal chip that embeds digitally programmable analog paral...
An efficient behavioral simulator for Cellular Neural Networks (CNN) is presented in this paper. The...
This paper presents a new computational framework to address the challenges in deeply scaled technol...
This paper presents a new computational framework to address the challenges in deeply scaled technol...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
This paper describes the design of a programmable Cellular Neural Network (CNN) chip, with additiona...
[eng] Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized ...
Cellular Neural Networks (CNN's) represent a remarkable improvement in hardware implementation of Ar...
The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Unive...
The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Unive...
Abstract. This paper describes a full-custom mixed-signal chip that embeds digitally programmable an...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
This paper present the application of Pulse Stream Tech-niques (PSTs) to the hardware implementation...
A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach...
In recent years, many applications have been implemented in embedded systems and mobile Internet of ...
This paper describes a full-custom mixed-signal chip that embeds digitally programmable analog paral...
An efficient behavioral simulator for Cellular Neural Networks (CNN) is presented in this paper. The...
This paper presents a new computational framework to address the challenges in deeply scaled technol...
This paper presents a new computational framework to address the challenges in deeply scaled technol...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
This paper describes the design of a programmable Cellular Neural Network (CNN) chip, with additiona...
[eng] Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized ...
Cellular Neural Networks (CNN's) represent a remarkable improvement in hardware implementation of Ar...