Advancing Design and Runtime Management of AI Applications with AI-SPRINT (Position Paper)
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
The adoption of Artificial intelligence (AI) technologies is steadily increasing. However, to become fully pervasive, AI needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge computing is close to where data are produced and therefore crucial to their timely, flexible, and secure management. In this paper, we introduce the AI-SPRINT project, which will provide solutions to seamlessly design, partition, and run AI applications in computing continuum environments. AI-SPRINT will offer novel tools for AI applications development, secure execution, easy deployment, as well as runtime management and optimization: AI-SPRINT design tools will allow trading-off application performance (in terms of end-toend latency or throughput), energy efficiency, and AI models accuracy while providing security and privacy guarantees. The runtime environment will support live data protection, architecture enhancement, agile delivery, runtime optimization, and continuous adaptation.
Details
Original language | English |
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Pages | 1455-1462 |
Number of pages | 8 |
Publication status | Published - Jul 2021 |
Peer-reviewed | Yes |
Conference
Title | 2021 IEEE 45th Annual Computers, Software, and Applications Conference |
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Abbreviated title | COMPSAC 2021 |
Conference number | 45 |
Duration | 12 - 16 July 2021 |
Website | |
Degree of recognition | International event |
City | Madrid |
Country | Spain |
External IDs
Scopus | 85115868821 |
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unpaywall | 10.1109/compsac51774.2021.00216 |
Mendeley | 60f0f586-997e-3190-bcac-223e1d504604 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Sustainable Development Goals
Keywords
- AI and machine learning, Cloud computing, Cloud trust security & privacy, edge computin, og computing, AI and machine learning, AI applications development, AI-SPRINT design tools, AI-SPRINT project, Biological system modeling, Cloud computing, Cloud trust security & privacy, Computational modeling, Privacy, artificial intelligence, artificial intelligence technologies, big data, data flow analysis, data protection, edge computing, fog computing, runtime environment, runtime management, runtime optimization, security of data