In-memory analog computing for non-negative matrix factorization.

ABSTRACT: Non-negative matrix factorization (NMF) is a powerful technique for extracting latent structures from high-dimensional data, with applications spanning recommender systems, bioinformatics, and image processing. However, conventional digital hardware struggles to efficiently handle large-scale NMF due to computational complexity and memory bottlenecks. In this work, we propose an in-memory analog NMF solver based on a reconfigurable compact generalized inverse circuit, optimized using the conductance compensation principle. This circuit significantly reduces power consumption by minimizing the number of operational amplifiers while supporting various non-negative regression constraints. By integrating the alternating non-negative least squares algorithm, we achieve efficient and accurate fa

SUBMITTER: Wang S 

PROVIDER: S-EPMC12923777 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature