@article{1476191,
      recid = {1476191},
      author = {Lee, Juhyoung, and Yoo, Hoi-Jun,},
      title = {Deep reinforcement learning processor design for mobile  applications /},
      pages = {1 online resource (vi, 101 pages) :},
      abstract = {This book discusses the acceleration of deep reinforcement  learning (DRL), which may be the next step in the burst  success of artificial intelligence (AI). The authors  address acceleration systems which enable DRL on  area-limited & battery-limited mobile devices. Methods are  described that enable DRL optimization at the algorithm-,  architecture-, and circuit-levels of abstraction. Enables  deep reinforcement learning (DRL) optimization at  algorithm-, architecture-, and circuit-levels of  abstraction; Includes methodologies that can reduce the  high cost of DRL; Uses analysis of computational workload  characteristics of DRL in the context of acceleration.},
      url = {http://library.usi.edu/record/1476191},
      doi = {https://doi.org/10.1007/978-3-031-36793-9},
}