POSTER: Application-Driven Near-Data Processing for Similarity Search

Abstract

Similarity search is a key to important applications such as content-based search, deduplication, natural language processing, computer vision, databases, and graphics. At its core, similarity search manifests as k-nearest neighbors (kNN) which consists of parallel distance calculations and a top-k sort. While kNN is poorly supported by today's architectures, it is ideal for near-data processing because of its high memory bandwidth requirements. This work proposes a near-data processing accelerator for similarity search: the similarity search associative memory (SSAM).