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2. Outline
Motivation and Introduction
Problem description
Our approach
Results and Conclusion

3. Motivation
DPR in UAVs (power saving)
Complex applications
Swarm (multiple FPGAs)
Problem definition (How do you develop adaptive applications in this scenario – so that they can migrate from one platform to another – need for a high level abstraction/support/framework; maintaining the parallism – mapping issues)

4. Motivation UAVs Swarm of UAVs Limited power
complex signal / image processing applications
FPGAs
Swarm of UAVs
Constant surveillance – xx days
Cooperative applications: tracking, geo-referencing, bush fire monitoring
Can we do distributed computing with multiple FPGAs?
Migrating applications – dying UAV
Larger application – distribute it in the Swarm

5. Motivation New App – lack of computational resources
Mobility of Reconfigurable Applications
If one UAV fails
Can the state of a FPGA be migrated during runtime?
New App – lack of computational resources
Distribute over a network of FPGAs
UAV – 1 (FPGA)
New App
UAV – 2 (FPGA)

6. Module based design of hardware – single platform ReconfigME
Introduction
Module based design of hardware – single platform ReconfigME
Platform dependency (like the slot machines – hard to develop application in true module based fashion – need a standardization for module reusability)
Module based development is hindered because – state saving problem and inter module data dependencies

7. Background Hardware modules Dynamic Reconfiguration
App
Hardware modules
Independent
Reusable
Third party
Dynamic Reconfiguration
Slot based
ReconfigME
Adaptive applications
Swapping of modules during runtime
Detection / Tracking
Netlist
ReconfigME

8. Application Development
1 – D and 2 – D reconfigurable framework exists
Runtime placement and routing
Not in the application development level
Application development is difficult
Need to be have core hardware knowledge
Even when third party modules are available; developers need to worry about state saving
Inter module communication
More complicated with multiple FPGAs
Need a higher level simple model

9. Kahn Process Network - KPN
Benefits
Suitability with this scenario
Limitations (unlimited buffers, limiting will bring local deadlock – but application developers can restrict the nature of the graphs – hence a more restricted KPN)

10. Details of the UAV platform
Lot of RAM if the buffering exceeds it can be done in software

11. Agent-based KPN nodes
The KPN nodes are treated as agents
Benefits – mapping problem solved
Rule based agents – migrate from one platform to another – hence an application will have autonomous KPN nodes distributed in the UAV swarm – brings high adaptability
Task migration from one platform to another
Failure detection

12. At the side give a brief description of ReconfigME An overall diagram
Overall Model
At the side give a brief description of ReconfigME
An overall diagram

13. Results
The blob tracking results – make sure this example is used through out the slide if possible

14. Discussion
The work represents a first try, yeat to be tested on a highly resourceful platform – speed, but can be improved on faster boards, custom designed high speed RF modems
Future work – the agents to be built as hardware and more work on fault tolerance.

15. Can we bring a standardization to the state saving problem ?
Conclusion
How to use a network of geographically separated FPGAs for streaming applications ?
Can we bring a standardization to the state saving problem ?
How to map applications over a number of FPGAs ?