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HIL: A Framework for Compositional FTL Development and Provably-Correct Crash Recovery.

Jin-yong ChoiEyee Hyun NamYoon Jae SeongJinhyuk YoonSookwan LeeHongseok KimJeongsu ParkYeong-Jae WooSheayun LeeSang Lyul Min
Published in: ACM Trans. Storage (2018)
Keyphrases
  • provably correct
  • main contribution
  • neural network
  • artificial intelligence
  • machine learning
  • case study
  • information processing
  • theoretical framework