PIMCQG: Co-Designing Billion-Scale Graph-Based Approximate Nearest Neighbor Search for Processing-in-Memory

Graph-based Approximate Nearest Neighbor Search (ANNS) has emerged as the dominant approach for large-scale AI applications, offering superior accuracy-efficiency trade-offs over alternative methods. However, graph-based ANNS is inherently memory-bound due to frequent and irregular graph traversal. This constrains CPU throughput due to limited memory bandwidth, while GPUs lack sufficient HBM capacity for billion-scale indexes. Processing-in-Memory (PIM) offers massive internal bandwidth by co-l…

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-04. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern