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Chair of Network Architectures and Services Department of Informatics Technical University of Munich
Structure
- Introduction
- Use cases
- Scheduling strategies
- G. Eickelpasch — DL on the edge
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Deep Learning on the mobile edge Georg Eickelpasch advised by - - PowerPoint PPT Presentation
Deep Learning on the mobile edge Georg Eickelpasch advised by - - PowerPoint PPT Presentation
Chair of Network Architectures and Services Department of Informatics Technical University of Munich Deep Learning on the mobile edge Georg Eickelpasch advised by Marton Kajo Thursday 10 th October, 2019 Chair of Network Architectures and
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Introduction
Problem of mobile Deep Learning
- Offloading to cloud
- Bottleneck bandwidth
- Unstable mobile connection
- Compute on device
- Compute using the edge
- G. Eickelpasch — DL on the edge
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Introduction
Terminology of Edge
- 1. Edge as mobile device -> offload from
- 2. Edge as local server -> offload to
Possible layers in an edge system
- G. Eickelpasch — DL on the edge
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Use cases
Speech
- Speech recognition
- Natural Language Processing
- Real-time processing
- Robustness
- Complex Data
- G. Eickelpasch — DL on the edge
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Use cases
Computer Vision
- Object identification
- Continuous Vision
- Real-time processing
- Mobile context
- Large files
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Use cases
E-Health
- Health Tracking
- Diseas identification
- Highly sensitive data
- Requiered robustness
- Heterogeneous data
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Scheduling
Preprocessing Strategy Simplified overview for NeuroSurgeon algorithm
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Scheduling
Preprocessing Strategy
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Scheduling
Deadline Strategy Simplified overview for Edgent algorithm
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Scheduling
Deadline Strategy
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Conclusion
- NeuroSurgeon
- only optimization
- weak edge
- Edgent
- best result in given time
- strong edge
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Future work
- Optimization of Edgent with NeuroSurgeon approach Preprocessing on the device as well
as early exit
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Discussion questions
- Do you think Edge Computing will be the dominant solution in the future?
- G. Eickelpasch — DL on the edge
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