On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
2005 Special issue: FPGA implementation of self organizing map with digital phase locked loops
Neural Networks - 2005 Special issue: IJCNN 2005
Controlling the magnification factor of self-organizing feature maps
Neural Computation
Microprocessors & Microsystems
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Asymptotic quantization error of continuous signals and the quantization dimension
IEEE Transactions on Information Theory
IP core implementation of a self-organizing neural network
IEEE Transactions on Neural Networks
A massively parallel architecture for self-organizing feature maps
IEEE Transactions on Neural Networks
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
IEEE Transactions on Neural Networks
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In this paper we compare a scalable FPGA-based hardware accelerator for the emulation of Self-Organizing Feature Maps (SOMs) with a multi-threaded software implementation on a state-of-the-art multi-core microprocessor. After discussing the mapping of SOMs to the reconfigurable digital hardware implementation, we present how the modular system architecture can be flexibly adapted to various application datasets as well as to variants of SOMs like Conscience SOM. Hyperspectral image processing is used as a benchmark scenario for the comparison of our FPGA-based hardware accelerator and state-of-the-art multi-core microprocessors. The hardware costs, power consumption, and scalability of the FPGA-based accelerator using Xilinx Virtex-4 FPGAs are discussed. For the real-world datasets used here, which require large SOMs, a speedup and energy reduction of one order of magnitude are achieved.