466
M. N. Bojnordi and P. Behnam
References
1. G. Bello-Orgaz, J.J. Jung, D. Camacho, Social big data: recent achievements and new
challenges. Inf. Fusion 28, 45–59 (2016)
2. E. Ahmed, I. Yaqoob, I.A.T. Hashem, I. Khan, A.I.A. Ahmed, M. Imran, A.V. Vasilakos, The
role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017)
3. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things:
a survey on enabling technologies, protocols, and applications, in IEEE Communications
Surveys & Tutorials, 2015
4. N. Koshizuka, K. Sakamura, Ubiquitous ID: Standards for Ubiquitous computing and the
Internet of Things, in IEEE Pervasive Comput., 2010
5. N. Kushalnagar, G. Montenegro, C. Schumacher, Pv6 over Low-Power Wireless Personal
Area Networks (6LoWPANs): overview, assumptions, problem statement, and goals, in
Internet Eng. Task Force (IETF), 2007
6. M. Kheirkhahan, S. Nair, A. Davoudi, P. Rashidi, A. Wanigatunga, D. Corbett, T. Mendoza,
T. Manini, S. Ranka, A smartwatch-based framework for real-time and online assessment and
mobility monitoring. J. Biomed. Informatics 89, 29–40 (2019)
7. P. Barnaghi, W. Wang, C. Henson, K. Taylor, Early progress and back to the future, in
International Journal on Semantic Web and Information Systems (IJSWIS), 2012
8. G. Kestor, R. Gioiosa, D.J. Kerbyson, A. Hoisie, Quantifying the energy cost of data
movement in scientific applications, in IEEE International Symposium on Workload Characterization (IISWC), 2013
9. D. Pandiyan, C.-J. Wu, Quantifying the energy cost of data movement for emerging smart
phone workloads on mobile platforms, in IEEE International Symposium on Workload
Characterization (IISWC), 2014
10. N. Chatterjee, M. O’Connor, D. Lee, D.R. Johnson, S.W. Keckler, M. Rhu, W.J. Dally,
Architecting an energy-efficient DRAM system for GPUs, in IEEE International Symposium
on High Performance Computer Architecture (HPCA), 2017.
11. “The top ten exascale research challenges,” Report of the Advanced Scientific Computing
Advisory Committee Subcommittee, 2014.
12. I. Akturk, U.R. Karpuzcu, Amnesiac: Amnesic automatic computer, in Proceedings of
the Twenty-Second International Conference on Architectural Support for Programming
Languages and Operating Systems, 2017
13. R. Balasubramonian, J. Chang, T. Manning, J.H. Moreno, R. Murphy, R. Nair, S. Swanson,
Near-data processing: insights from a micro-46 workshop. IEEE Micro 2, 36–42 (2014)
14. K. Lim, J. Chang, T. Mudge, P. Ranganathan, S.K. Reinhardt, T.F. Wenisch, Disaggregated
memory for expansion and sharing in blade servers, in International Symposium on Computer
Architecture, 2009
15. Y. Chen, T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun, Dadiannao: a
machine-learning supercomputer, in Proceedings of the 47th Annual IEEE/ACM International
Symposium on Microarchitecture, 2014
16. J.C. Beyler, et al., ESODYP: an entirely software and dynamic data prefetcher based on a
Markov model, in 12th Workshop on Compilers for Parallel Computers, 2006
17. X. Yu, C.J. Hughes, N. Satish, S. Devadas, IMP: indirect memory prefetcher, in Proceedings
of the 48th International Symposium on Microarchitecture, 2015
18. J. Jeddeloh, B. Keeth, Hybrid memory cube new DRAM architecture increases density and
performance, in Symposium on VLSI Technology (VLSIT), 2012.
19. J. Kim, J.S. Pak, J. Cho, E. Song, J. Cho, H. Kim, T. Song, J. Lee, H. Lee, K. Park, et al.,
High-frequency scalable electrical model and analysis of a through silicon via (TSV). IEEE
Trans. Compon. Packag. Manuf. Technol. 1(2), 181–195 (2011)
20. R. Hameed, W. Qadeer, M. Wachs, O. Azizi, A. Solomatnikov, B.C. Lee, S. Richardson,
C. Kozyrakis, M. Horowitz, Understanding sources of inefficiency in general-purpose chips.
ACM SIGARCH Comput. Archit. News 38(3), 37–47 (2010)
M. N. Bojnordi and P. Behnam
References
1. G. Bello-Orgaz, J.J. Jung, D. Camacho, Social big data: recent achievements and new
challenges. Inf. Fusion 28, 45–59 (2016)
2. E. Ahmed, I. Yaqoob, I.A.T. Hashem, I. Khan, A.I.A. Ahmed, M. Imran, A.V. Vasilakos, The
role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017)
3. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things:
a survey on enabling technologies, protocols, and applications, in IEEE Communications
Surveys & Tutorials, 2015
4. N. Koshizuka, K. Sakamura, Ubiquitous ID: Standards for Ubiquitous computing and the
Internet of Things, in IEEE Pervasive Comput., 2010
5. N. Kushalnagar, G. Montenegro, C. Schumacher, Pv6 over Low-Power Wireless Personal
Area Networks (6LoWPANs): overview, assumptions, problem statement, and goals, in
Internet Eng. Task Force (IETF), 2007
6. M. Kheirkhahan, S. Nair, A. Davoudi, P. Rashidi, A. Wanigatunga, D. Corbett, T. Mendoza,
T. Manini, S. Ranka, A smartwatch-based framework for real-time and online assessment and
mobility monitoring. J. Biomed. Informatics 89, 29–40 (2019)
7. P. Barnaghi, W. Wang, C. Henson, K. Taylor, Early progress and back to the future, in
International Journal on Semantic Web and Information Systems (IJSWIS), 2012
8. G. Kestor, R. Gioiosa, D.J. Kerbyson, A. Hoisie, Quantifying the energy cost of data
movement in scientific applications, in IEEE International Symposium on Workload Characterization (IISWC), 2013
9. D. Pandiyan, C.-J. Wu, Quantifying the energy cost of data movement for emerging smart
phone workloads on mobile platforms, in IEEE International Symposium on Workload
Characterization (IISWC), 2014
10. N. Chatterjee, M. O’Connor, D. Lee, D.R. Johnson, S.W. Keckler, M. Rhu, W.J. Dally,
Architecting an energy-efficient DRAM system for GPUs, in IEEE International Symposium
on High Performance Computer Architecture (HPCA), 2017.
11. “The top ten exascale research challenges,” Report of the Advanced Scientific Computing
Advisory Committee Subcommittee, 2014.
12. I. Akturk, U.R. Karpuzcu, Amnesiac: Amnesic automatic computer, in Proceedings of
the Twenty-Second International Conference on Architectural Support for Programming
Languages and Operating Systems, 2017
13. R. Balasubramonian, J. Chang, T. Manning, J.H. Moreno, R. Murphy, R. Nair, S. Swanson,
Near-data processing: insights from a micro-46 workshop. IEEE Micro 2, 36–42 (2014)
14. K. Lim, J. Chang, T. Mudge, P. Ranganathan, S.K. Reinhardt, T.F. Wenisch, Disaggregated
memory for expansion and sharing in blade servers, in International Symposium on Computer
Architecture, 2009
15. Y. Chen, T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun, Dadiannao: a
machine-learning supercomputer, in Proceedings of the 47th Annual IEEE/ACM International
Symposium on Microarchitecture, 2014
16. J.C. Beyler, et al., ESODYP: an entirely software and dynamic data prefetcher based on a
Markov model, in 12th Workshop on Compilers for Parallel Computers, 2006
17. X. Yu, C.J. Hughes, N. Satish, S. Devadas, IMP: indirect memory prefetcher, in Proceedings
of the 48th International Symposium on Microarchitecture, 2015
18. J. Jeddeloh, B. Keeth, Hybrid memory cube new DRAM architecture increases density and
performance, in Symposium on VLSI Technology (VLSIT), 2012.
19. J. Kim, J.S. Pak, J. Cho, E. Song, J. Cho, H. Kim, T. Song, J. Lee, H. Lee, K. Park, et al.,
High-frequency scalable electrical model and analysis of a through silicon via (TSV). IEEE
Trans. Compon. Packag. Manuf. Technol. 1(2), 181–195 (2011)
20. R. Hameed, W. Qadeer, M. Wachs, O. Azizi, A. Solomatnikov, B.C. Lee, S. Richardson,
C. Kozyrakis, M. Horowitz, Understanding sources of inefficiency in general-purpose chips.
ACM SIGARCH Comput. Archit. News 38(3), 37–47 (2010)