A Feature-Major Codebook for Memory-Efficient Sparse-Binary Self-Organizing Maps: Scaling a MEDLINE Atlas to 1.05 Million Neurons on a Single Consumer GPU
Read the original on arXiv Machine Learning →The paper presents a memory‑efficient sparse‑binary self‑organising map (SOM) that scales a MEDLINE atlas to over a million neurons on a single consumer GPU. By re‑ordering the codebook into a feature‑major layout, the authors accelerate the best‑matching‑unit search by 4.5–8.5× without increasing quantisation error, enabling training of a 1,048,576‑neuron SOM in 72 s on a 24 GB GPU. The approach outperforms existing cuSPARSE and CPU‑based SOM implementations, achieving the largest SOM reported to date and demonstrating that resolution limits are computational rather than data‑driven.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.