gNBXe - a Reconfigurable Neuroprocessor for Various Types of Self-Organizing Maps

In this paper we present the FPGA-based hardware accelerator gNBXe for emulation of classical Self-Organizing Maps (SOMs) and Conscience SOM (CSOM) in a multi-FPGA environment. After discussing how the CSOM is mapped to a resource-efficient digital hardware implementation, we present how the modular system architecture can be flexibly adapted to various application datasets. The hardware costs and scalability of a multi-FPGA based accelerator using Xilinx Virtex2 and Virtex4 FPGAs are discussed. Compared to a state-of-the-art multi-core PC, a speedup of 9.1 is achieved for a CSOM with 4, 840 neurons and 196 synaptic weights.