16420

Near Memory Similarity Search on Automata Processors

Vincent T. Lee, Justin Kotalik, Carlo C. Del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin
University of Washington
arXiv:1608.03175 [cs.DC], (9 Aug 2016)
@article{lee2016near,

   title={Near Memory Similarity Search on Automata Processors},

   author={Lee, Vincent T. and Kotalik, Justin and Mundo, Carlo C. Del and Alaghi, Armin and Ceze, Luis and Oskin, Mark},

   year={2016},

   month={aug},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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Embedded devices and multimedia applications today generate unprecedented volumes of data which must be indexed and made searchable. As a result, similarity search has become a critical idiom for many modern data intensive applications in natural language processing (NLP), vision, and robotics. At its core, similarity search is implemented using k-nearest neighbors (kNN) where computation consists of highly parallel distance calculations and a global top-k sort. In contemporary von-Neumann architectures, kNN is bottlenecked by data movement limiting throughput and latency.
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Near Memory Similarity Search on Automata Processors, 4.3 out of 5 based on 29 ratings

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