A Flexible and High-Performance Self-Organizing Feature Map Training Acceleration Circuit and Its Applications.
Journal
IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Hsinchu, Taiwan, March 18-20, 2019
Pages
92-96
Date Issued
2019
Author(s)
Sun, Yu-Hsiu
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.
SDGs
Type
conference paper