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Trends of Compiler Development for AI Processor

인공지능 프로세서 컴파일러 개발 동향

  • 김진규 (인공지능프로세서연구실) ;
  • 김혜지 (인공지능프로세서연구실) ;
  • 조용철 (인공지능프로세서연구실) ;
  • 김현미 (인공지능프로세서연구실) ;
  • 여준기 (인공지능프로세서연구실) ;
  • 한진호 (인공지능프로세서연구실) ;
  • 권영수 (지능형반도체연구본부)
  • Published : 2021.04.01

Abstract

The rapid growth of deep-learning applications has invoked the R&D of artificial intelligence (AI) processors. A dedicated software framework such as a compiler and runtime APIs is required to achieve maximum processor performance. There are various compilers and frameworks for AI training and inference. In this study, we present the features and characteristics of AI compilers, training frameworks, and inference engines. In addition, we focus on the internals of compiler frameworks, which are based on either basic linear algebra subprograms or intermediate representation. For an in-depth insight, we present the compiler infrastructure, internal components, and operation flow of ETRI's "AI-Ware." The software framework's significant role is evidenced from the optimized neural processing unit code produced by the compiler after various optimization passes, such as scheduling, architecture-considering optimization, schedule selection, and power optimization. We conclude the study with thoughts about the future of state-of-the-art AI compilers.

Keywords

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