Presentation
MemSearch: An Efficient Memristive In-memory Search Engine with Configurable Similarity Measures
DescriptionIn-memory search has emerged as a promising solution for efficient vector discovery of the nearest neighbors in general-purpose vector databases. However, templated storage-in-array structure and VMM-based computational form of in-memory search pose challenges in supporting generic distance computations. In this work, for the first time, we introduce a novel memristive in-memory similarity measure engine, MemSearch, for configurable distance calculations, including dot distance, ED, and CD. MemSearch highlights two aspects: data storage and distance computing. For data storage, we propose a Unified Similarity Element Mapping (USEM) scheme based on a pair array to accommodate various similarity calculations. For distance computing, we introduce a Reconfigurable Current Computing (RCC) circuit designed to process multiple arithmetic rules in similarity calculations, with a slightly increase of 4.4% and 9.9% in energy consumption for ED and CD, respectively. We have tested various datasets with different modalities, including images, voice, human activity and text. Experimental results demonstrate that the MemSearch engine achieves improvements of 864×, 802×, and 1474× in energy efficiency over CMOS-based engines for dot distance, ED, and CD calculations, respectively. The MemSearch engine highlights its potential for future highly efficient general-purpose in-memory vector databases.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby
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