A Massively Parallel Implementation of a Modular Self-Organizing Map on FPGAs

This paper describes the architecture design of novel massively parallel self-organizing map (SOM) neural networks. The proposed architecture, referred to as the planar SOM (PSOM), is described as a soft IP core synthesized in VHDL. The SOM neural network's size and the input data vectors' dimension are adjustable parameters. In this work, several SOM architectures are synthesized and their performance is evaluated for Xilinx Virtex-7 FPGAs. The presented hardware architecture allows online learning and can be easily adapted to a large variety of SOM topologies without a considerable design effort. A 16 x 16 SOM hardware is validated through the FPGA implementation and its performances with an estimated working frequency of 297 MHz for a 23-element input vector will reach 21,970 MCUPS in the learning phase and 35,902 MCPS in the recall one.