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University of South Australia Distributed Reconfiguration Avishek Chakraborty, David Kearney, Mark Jasiunas.

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Presentation on theme: "University of South Australia Distributed Reconfiguration Avishek Chakraborty, David Kearney, Mark Jasiunas."— Presentation transcript:

1 University of South Australia Distributed Reconfiguration Avishek Chakraborty, David Kearney, Mark Jasiunas

2 Outline  Motivation  Background  Our approach  Experiment  Conclusion

3 Motivation UAVs  Limited power  complex signal / image processing applications  FPGAs Swarm of UAVs  Constant surveillance – 52 hrs*  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

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

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

6 Background ReconfigME –A preemptive framework –A proxy based connection between SW and HW running on the FPGA board –Built to support streaming applications but application developers had to do the check pointing tasks Currently there are quite a few number of frameworks that supports slot / tile based reconfiguration ReconfigME

7 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 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

8 Kahn Process Network - KPN Process Networks –Communicates via infinite FIFOs –Writing channel is unblocking but reading is blocking Benefits –Deterministic –Concurrency can be implemented safely –Inherently distributed in nature –Distributed memory Suits with module based design –Applications can be easily represented as KPN nodes

9 Kahn Process Network - KPN KPN buffers as registers –Application states are stored in registers –If the buffers are saved then application can be preempted restarted again from the same position –Hence the whole application can be migrated to another FPGA A high-level framework is needed –Storing / restoring of buffers –Connection between the modules –To use the modules in a plug and play fashion b a d c Capturing State of a distributed application

10 Kahn Process Network - KPN Implementation KPN buffers cannot be infinite –Though some have proposed virtually infinite buffers –The above is directed acyclic graph that may experience deadlock (Thomas M. Parks 1987) –Such constructs can be avoided –In KahnME buffering is done in software but this can be done in HW as well Buffer Limits

11 The KPN nodes as Agents –The nodes are autonomous –Migrates to the most suitable platform –A solution for distributed mapping Agent encapsulates the KPN node as well as the input buffer –Making them mobile with the state –Migrate from one FPGA to another Agent-based KPN nodes KPN node State-less State-full Buffer

12 Rule based Agents –Conditions for migration –The rule engine runs in a separate thread –Rules are written in a XML file Consists of HW and SW part –The SW part consists of the API to connect to the FPGA and the Migration Control Thread –HW part is the module to be placed on the FPGA Agent-based KPN nodes Processing Thread Migration Control Thread StatesModules ReconfigME API Rules Events Reactions Agent Platform

13 An agent nodes can be classified as: –Source, Sink, and Process –Source / Sink virtualizes the underlying hardware, like drivers –Basic elements of any application Subtle issues: migration of source and sinks –Can be migrated! but not physically –The agent will look for a similar source/sink in the swarm –What if the platform along with source / sink node is dies ? –Failure detections Agent-based KPN nodes Source Sink Process

14 Overall Model KahnME FPGA ReconfigME

15 Blue – B tracking experiment –Two Stationary UAV platforms and another Ground-Station –Each UAV platforms having Vertex 1000E FPGA (I know very slow  ) –The tracking application searches for a Blue-B Blue-B Tracker –The application is launched in the Ground-Station –it soon starts searching for appropriate platform –Once the Blue-B is found the agent will migrate to another platform if lost again; we did this by manually moving the camera Tracking Experiment

16 The three platforms

17 Migration Issues –The migrating agent’s size was 360kB. –Size of the sates 1500 bytes –Rest was of the Blue-B template !! (should have saved it locally but not realistic) –The migrating module consisted of 18.2 kB SW and 492 kB HW (EDIF) –Hence any typical agent will need around 1.5 kB to 672 kB of data during the migration More Improvements –By compressing the data –In practical situation migrations will be less –Newer board Tracking Experiment Results

18 This work represents a first try to run application in a geographically distributed network of FPGAS – Distributed Reconfiguration We have argued that there is a need to standardized state saving techniques to built frameworks for DPR – reusable modules We have proposed that KPN buffers can be treated as registers holding states We have proposed to treat KPN nodes as autonomous agents to solve the problem of distributed reconfiguration Conclusion

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