@INPROCEEDINGS{CODES2019, 
  author    = {Paul Bogdan and
               {<a href="https://homes.luddy.indiana.edu/fc7/" target="_blank">Fan Chen</a></span>} and
               Aryan Deshwal and
               Janardhan Rao Doppa and
               Biresh Kumar Joardar and
               Hai Helen Li and
               Shahin Nazarian and
               Linghao Song and
               Yao Xiao},
  title     = {Taming extreme heterogeneity via machine learning based design of
               autonomous manycore systems},
  booktitle = {Proceedings of the International Conference on Hardware/Software Codesign
               and System Synthesis Companion (CODES+ISSS)},
  pages     = {21:1--21:10},
  publisher = {{ACM}},
  year      = {2019},
  doi       = {10.1145/3349567.3357376}, 
  abstract  = {To avoid rewriting software code for new computer architectures and to take advantage of the extreme heterogeneous processing, communication and storage technologies, there is an urgent need for determining the right amount and type of specialization while making a heterogeneous system as programmable and flexible as possible. To enable both programmability and flexibility in the heterogeneous computing era, we propose a novel complex network inspired model of computation and efficient optimization algorithms for determining the optimal degree of parallelization from old software code. This mathematical framework allows us to determine the required number and type of processing elements, the amount and type of deep memory hierarchy, and the degree of reconfiguration for the communication infrastructure, thus opening new avenues to performance and energy efficiency. Our framework enables heterogeneous manycore systems to autonomously adapt from traditional switching techniques to network coding strategies in order to sustain on-chip communication in the order of terabytes. While this new programming model enables the design of self-programmable autonomous heterogeneous manycore systems, a number of open challenges will be discussed.},
}

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H.","Nazarian, S.","Song, L.","Xiao, Y."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Paul"],"propositions":[],"lastnames":["Bogdan"],"suffixes":[]},{"firstnames":[],"propositions":[],"lastnames":["<a href=\"https://homes.luddy.indiana.edu/fc7/\" target=\"_blank\">Fan Chen</a></span>"],"suffixes":[]},{"firstnames":["Aryan"],"propositions":[],"lastnames":["Deshwal"],"suffixes":[]},{"firstnames":["Janardhan","Rao"],"propositions":[],"lastnames":["Doppa"],"suffixes":[]},{"firstnames":["Biresh","Kumar"],"propositions":[],"lastnames":["Joardar"],"suffixes":[]},{"firstnames":["Hai","Helen"],"propositions":[],"lastnames":["Li"],"suffixes":[]},{"firstnames":["Shahin"],"propositions":[],"lastnames":["Nazarian"],"suffixes":[]},{"firstnames":["Linghao"],"propositions":[],"lastnames":["Song"],"suffixes":[]},{"firstnames":["Yao"],"propositions":[],"lastnames":["Xiao"],"suffixes":[]}],"title":"Taming extreme heterogeneity via machine learning based design of autonomous manycore systems","booktitle":"Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion (CODES+ISSS)","pages":"21:1–21:10","publisher":"ACM","year":"2019","doi":"10.1145/3349567.3357376","abstract":"To avoid rewriting software code for new computer architectures and to take advantage of the extreme heterogeneous processing, communication and storage technologies, there is an urgent need for determining the right amount and type of specialization while making a heterogeneous system as programmable and flexible as possible. To enable both programmability and flexibility in the heterogeneous computing era, we propose a novel complex network inspired model of computation and efficient optimization algorithms for determining the optimal degree of parallelization from old software code. This mathematical framework allows us to determine the required number and type of processing elements, the amount and type of deep memory hierarchy, and the degree of reconfiguration for the communication infrastructure, thus opening new avenues to performance and energy efficiency. Our framework enables heterogeneous manycore systems to autonomously adapt from traditional switching techniques to network coding strategies in order to sustain on-chip communication in the order of terabytes. While this new programming model enables the design of self-programmable autonomous heterogeneous manycore systems, a number of open challenges will be discussed.","bibtex":"@INPROCEEDINGS{CODES2019, \r\n author = {Paul Bogdan and\r\n {<a href=\"https://homes.luddy.indiana.edu/fc7/\" target=\"_blank\">Fan Chen</a></span>} and\r\n Aryan Deshwal and\r\n Janardhan Rao Doppa and\r\n Biresh Kumar Joardar and\r\n Hai Helen Li and\r\n Shahin Nazarian and\r\n Linghao Song and\r\n Yao Xiao},\r\n title = {Taming extreme heterogeneity via machine learning based design of\r\n autonomous manycore systems},\r\n booktitle = {Proceedings of the International Conference on Hardware/Software Codesign\r\n and System Synthesis Companion (CODES+ISSS)},\r\n pages = {21:1--21:10},\r\n publisher = {{ACM}},\r\n year = {2019},\r\n doi = {10.1145/3349567.3357376}, \r\n abstract = {To avoid rewriting software code for new computer architectures and to take advantage of the extreme heterogeneous processing, communication and storage technologies, there is an urgent need for determining the right amount and type of specialization while making a heterogeneous system as programmable and flexible as possible. To enable both programmability and flexibility in the heterogeneous computing era, we propose a novel complex network inspired model of computation and efficient optimization algorithms for determining the optimal degree of parallelization from old software code. This mathematical framework allows us to determine the required number and type of processing elements, the amount and type of deep memory hierarchy, and the degree of reconfiguration for the communication infrastructure, thus opening new avenues to performance and energy efficiency. Our framework enables heterogeneous manycore systems to autonomously adapt from traditional switching techniques to network coding strategies in order to sustain on-chip communication in the order of terabytes. While this new programming model enables the design of self-programmable autonomous heterogeneous manycore systems, a number of open challenges will be discussed.},\r\n}\r\n\r\n","author_short":["Bogdan, P.","<a href=\"https://homes.luddy.indiana.edu/fc7/\" target=\"_blank\">Fan Chen</a></span>","Deshwal, A.","Doppa, J. R.","Joardar, B. K.","Li, H. 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