Rüping S, Goser K, Rückert U. A Chip for Selforganizing Feature Maps. IEEE Micro. 1995;15(3):57-59.The use of self-organizing feature maps in real-time applications requires a high computational performance. Especially for embedded systems neural network chips are needed. In this paper a fabricated integrated circuit for self-organizing feature maps is presented. The architecture of this digital chip is based on the idea, that restrictions to the algorithm can simplify the implementation. Using the Manhattan Distance and a special treatment of the adaptation factor α decreases the necessary chip area, so that a high number of processor elements can be integrated on one chip. The effects of these restrictions on the function of the self-orga...
A new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Koh...
The paper presents a method for FPGA implementation of Self-Organizing Map (SOM) artificial neural n...
Rüping S, Porrmann M, Rückert U. SOM Accelerator System. Neurocomputing. 1998;21:31-50.Many applicat...
Rüping S, Rückert U, Goser K. A Chip for Selforganizing Feature Maps. In: Proceedings of the 4th In...
Rüping S, Rückert U. A Scalable Processor Array for Self-Organizing Feature Maps. In: Proceedings o...
Porrmann M, Witkowski U, Rückert U. A Massively Parallel Architecture for Self-Organizing Feature Ma...
Porrmann M, Kalte H, Witkowski U, Niemann J-C, Rückert U. A Dynamically Reconfigurable Hardware Acce...
Rüping S, Porrmann M, Rückert U. A High Performance SOFM Hardware-System. In: Proceedings of the In...
Lachmair J, Merényi E, Porrmann M, Rückert U. A reconfigurable neuroprocessor for self-organizing fe...
In this paper we present a hardware realization of Kohonen's selforganizing feature map as a ne...
Porrmann M, Ruping S, Rückert U. SOM hardware with acceleration module for graphical representation ...
The motivation for this research is to be able to replicate a simplified neuronal model onto an FPGA...
In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neu...
Abstract. A dynamically reconfigurable hardware accelerator for self-organizing feature maps is pres...
Nowadays, one of the main challenges in computer architectures is scalability; indeed, novel process...
A new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Koh...
The paper presents a method for FPGA implementation of Self-Organizing Map (SOM) artificial neural n...
Rüping S, Porrmann M, Rückert U. SOM Accelerator System. Neurocomputing. 1998;21:31-50.Many applicat...
Rüping S, Rückert U, Goser K. A Chip for Selforganizing Feature Maps. In: Proceedings of the 4th In...
Rüping S, Rückert U. A Scalable Processor Array for Self-Organizing Feature Maps. In: Proceedings o...
Porrmann M, Witkowski U, Rückert U. A Massively Parallel Architecture for Self-Organizing Feature Ma...
Porrmann M, Kalte H, Witkowski U, Niemann J-C, Rückert U. A Dynamically Reconfigurable Hardware Acce...
Rüping S, Porrmann M, Rückert U. A High Performance SOFM Hardware-System. In: Proceedings of the In...
Lachmair J, Merényi E, Porrmann M, Rückert U. A reconfigurable neuroprocessor for self-organizing fe...
In this paper we present a hardware realization of Kohonen's selforganizing feature map as a ne...
Porrmann M, Ruping S, Rückert U. SOM hardware with acceleration module for graphical representation ...
The motivation for this research is to be able to replicate a simplified neuronal model onto an FPGA...
In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neu...
Abstract. A dynamically reconfigurable hardware accelerator for self-organizing feature maps is pres...
Nowadays, one of the main challenges in computer architectures is scalability; indeed, novel process...
A new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Koh...
The paper presents a method for FPGA implementation of Self-Organizing Map (SOM) artificial neural n...
Rüping S, Porrmann M, Rückert U. SOM Accelerator System. Neurocomputing. 1998;21:31-50.Many applicat...