Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization.

ABSTRACT: A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaO_x/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the wei

SUBMITTER: Wang R 

PROVIDER: S-EPMC9051161 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature