A Flexible and High-Performance Self-Organizing Feature Map Training Acceleration Circuit and Its Applications

Abstract

Self-organizing feature map (SOFM) is a type of artificial neural network based on an unsupervised learning algorithm. In this work, we present a circuit for accelerating SOFM training, which forms the foundation for an effective, efficient, and flexible SOFM training platform for different network geometries, including array, rectangular, and binary tree. FPGA validation was also conducted to examine the speedup ratio of this circuit when compared with training using software. In addition, we applied our design to three applications: chromaticity diagram learning, MNIST handwritten numeral auto-labeling, and image vector quantization. All three experiments show that the proposed circuit architecture indeed provides a high-performance and cost-effective solution to SOFM training.