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@inproceedings{TUW-266014,
    author = {Shafique, Muhammad and Hafiz, Rehan and Javed, Muhammad Usama and Abbas, Sarmad and Sekanina, Lukas and Vasicek, Zdenek and Mrazek, Vojtech},
    title = {Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap},
    booktitle = {Proceedings of 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI'17)},
    year = {2017},
    pages = {617--632},
    journal = {IEEE},
    issn = {2159-3477},
    doi = {10.1109/ISVLSI.2017.124},
    keywords = {machine learning, approximate computing, deep learning, neural networks, energy efficiency, performance, low power, accelerators, architecture, memory, FPGA, CGRA, adaptive, roadmap},
    note = {talk: 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI'17), 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI'17); 2017-07-03 -- 2017-07-05}
}


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