TY - GEN
T1 - Eugene
T2 - 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
AU - Yao, Shuochao
AU - Jayarajah, Kasthuri
AU - Misra, Archan
AU - Abdelzaher, Tarek
AU - Hao, Yifan
AU - Zhao, Yiran
AU - Piao, Ailing
AU - Shao, Huajie
AU - Liu, Dongxin
AU - Liu, Shengzhong
AU - Hu, Shaohan
AU - Weerakoon, Dulanga
N1 - Funding Information:
This material is supported partially by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centers in Singapore Funding Initiative. The research reported in this paper was also sponsored in part by NSF under grants CNS 16-18627 and CNS 13-20209 and in part by the US Army Research Laboratory under Cooperative Agreements W911NF-09-2-0053 and W911NF-17-2-0196. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory, NSF, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The paper discusses an emerging suite of machine intelligence services that are of increasing importance in the highly instrumented world of the Internet of Things (IoT). The suite, called Eugene, would offer a form of intelligent behavior (based on deep neural networks) to otherwise simple embedded devices; the clients of the service. These devices would benefit from service resources to learn from data and to perform intelligent inference, classification, prediction, and estimation tasks that they are too limited to carry out on their own. The paper discusses the taxonomy of such services and the state of implementation, as well as the various challenges entailed, including scheduling, caching (of intelligent functions), and cooperative learning.
AB - The paper discusses an emerging suite of machine intelligence services that are of increasing importance in the highly instrumented world of the Internet of Things (IoT). The suite, called Eugene, would offer a form of intelligent behavior (based on deep neural networks) to otherwise simple embedded devices; the clients of the service. These devices would benefit from service resources to learn from data and to perform intelligent inference, classification, prediction, and estimation tasks that they are too limited to carry out on their own. The paper discusses the taxonomy of such services and the state of implementation, as well as the various challenges entailed, including scheduling, caching (of intelligent functions), and cooperative learning.
KW - Edge computing
KW - Internet of Things
KW - Machine intelligence
UR - http://www.scopus.com/inward/record.url?scp=85072559448&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072559448&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2019.00162
DO - 10.1109/ICDCS.2019.00162
M3 - Conference contribution
AN - SCOPUS:85072559448
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 1630
EP - 1640
BT - Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 July 2019 through 9 July 2019
ER -