Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.05a
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  • Pages.31-34
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Accelerating and analyzing the Recommendation System using Processing-in-Memory

Processing-in-Memory 를 이용한 추천시스템 가속화 및 분석

  • Jung-uk Hong (Dept. of Electrical and Computer Engineering, Seoul National University ) ;
  • Jin-ho Lee (Dept. of Electrical and Computer Engineering, Seoul National University)
  • 홍정욱 (서울대학교 전기정보공학부 ) ;
  • 이진호 (서울대학교 전기정보공학부)
  • Published : 2024.05.23
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Abstract

추천 시스템(Recommendation System)은 인터넷 쇼핑몰, 넷플릭스, SNS 등 여러 분야에서 유저에게 맞는 타겟 광고를 추천하는 시스템을 말한다. 추천 시스템을 가속하기 위해서는 추천 시스템 모델에서 불규칙적이고 잦은 데이터 이동으로 인해 병목현상을 일으키는 임베딩 레이어를 타겟하는 것이 중요하다고 알려져 있다. 이 논문에서는 데이터 이동이 잦은 어플리케이션에 효과적인 Processing-in-Memory 를 이용하여 추천 시스템을 가속하고 분석한다.

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References

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