Embedded devices operate under severe RAM constraints, which means both machine learning models and their search indexes often must reside in persistent storage, typically flash memory. A database management system (DBMS) with direct flash access plays a key role in this setting, offering persistence and structured access to ANN indexes while minimizing reliance on RAM. These constraints, however, expose the limits of many approximate nearest neighbor (ANN) algorithms.

One prominent example is RAM-based HNSW, which—though popular—consumes too much memory to be practical on small devices, while persistent approaches such as DiskANN, built on Vamana, are optimized for massive cloud-scale datasets and do not translate well to embedded workloads.

This paper reviews why classical database indexes such as R-trees do not scale to high-dimensional embeddings. It then outlines the evolution of ANN indexes for vector search:

• Inverted File (IVF) structures: Exploring clustering and centroid-based search.
• NSW, HNSW, and Vamana: Tracing the shift toward graph-based navigation.
• Embedded Adaptations: Identifying which approaches are most disk-friendly for Edge AI.

We examine factors like Product Quantization (PQ), which reduces size at the cost of accuracy, and adaptations of the Vamana algorithm for smaller datasets, including bounded-degree constraints. We argue that flash-adapted HNSW is a practical path: it can be implemented directly on persistent storage while delivering efficient search speed and recall.

We conclude with results from real flash hardware, comparing HNSW, Vamana, and IVF-PQ in terms of accuracy, latency, and resource usage, demonstrating that DBMS-backed ANN indexes on flash are the practical choice for embedded AI.