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Papers Published
- Pasricha, S; Doppa, JR; Chakrabarty, K; Tiku, S; Dauwe, D; Jin, S; Pande, PP, Special session paper: Data analytics enables energy-efficiency and robustness: From mobile to manycores, datacenters, and networks,
2017 International Conference on Hardware/Software Codesign and System Synthesis, Codes+Isss 2017
(November, 2017), ACM Press [doi] .
(last updated on 2022/12/30)Abstract:
The amount of data generated and collected across computing platforms every day is not only enormous, but growing at an exponential rate. Advanced data analytics and machinelearning techniques have become increasingly essential to analyze and extract meaning from such Big Data. These techniques can be very useful to detect patterns and trends to improve the operational behavior of computing platforms, but they also introduce a number of outstanding challenges: (1) How can we design and deploy data analytics and learning mechanisms to improve energy-efficiency in IoT and mobile devices, without introducing significant software overheads' (2) How to use machine learning and analytics techniques for effective designspace exploration during manycore chip design' (3) How can data analytics and learning improve the reliability and energyefficiency of large-scale cloud datacenters, to cost-effectively support connected embedded and IoT platforms? (4) How can data analytics detect anomalies and increase robustness in the network backbone of emerging cloud datacenter networks' In this paper, we discuss these outstanding problems and describe far-reaching solutions applicable across the interconnected ecosystem of IoT and mobile devices, manycore chips, datacenters, and networks.