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DTSTART:19700308T020000
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DTSTAMP:20260522T150111Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post107@linklings.com
SUMMARY:Processing-in-Storage Architecture for Machine Learning and Bioinf
 ormatics
DESCRIPTION:Roman Kaplan, Leonid Yavits, and Ran Ginosar (Israel Institute
  of Technology)\n\nUser-generated and bioinformatics database volumes has 
 been increasing exponentially for more than a decade. With the slowdown an
 d approaching end of Moore's law, traditional technologies cannot satisfy 
 the increasing demands for processing power.   This work presents PRINS, a
  highly-parallel in-storage processing architecture. PRINS combines non-vo
 latile memory with processing capabilities on every bitcell. An emerging t
 echnology, memristors, form the basis for the design. \n\nImplementations 
 of three data-intensive and massively parallel algorithms are demonstrated
 : (1) Smith-Waterman DNA local sequence alignment (bioinformatics), (3) K-
 means clustering (machine learning) and (3) data deduplication. Performanc
 e and energy efficiency of PRINS compared to other published solutions is 
 presented for each algorithm. PRINS is shown to achieve orders-of-magnitud
 e improvement in performance and power efficiency over existing solutions,
  from large-scale bioinformatics and machine-learning to single-GPU or FPG
 A implementations.\n\nRegistration Category: Tech Program Reg Pass, Exhibi
 ts Reg Pass\n\n
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