1. Evolvable Fuzzy Hardware for Real-time Embedded Control in Packet Switching Ju Hui Li Meng Hiot Lim Qi Cao

2. 11/21/2016 2 Outline  Introduction to EHW  Evolvable Fuzzy Hardware (EFH)  Hardware Implementation (RFIC)  ATM Cell-Scheduling  EFH application on Cell-Scheduling  Simulation Results  Conclusion and Future Work Nanyang Technological University, Singapore

3. 11/21/2016 3 Introduction to EHW  Definition Modify Autonomously  Classification Methods Methods of Evolution Extrinsic, Intrinsic Adaptation Scheme On-line, Off-line Evolutionary Granularity Transistor, Gate, Function Units Nanyang Technological University, Singapore

4. 11/21/2016 4 Introduction to EHW  Open Issues On-line Adaptation Scalability Termination of Evolution Nanyang Technological University, Singapore

5. 11/21/2016 5 Evolvable Fuzzy Hardware  Evolvable Fuzzy Hardware (EFH) Traditional Fuzzy Hardware Fuzzy Rule Set Designed by Experts Considering The Whole Scenario Fuzzy Rule Set Fixed EFH Fuzzy Rule Set Searched by GA The Small Period Scenario Fuzzy Rule Set may change Nanyang Technological University, Singapore

6. 11/21/2016 6 Evolvable Fuzzy Hardware  Architecture Evaluation Nanyang Technological University, Singapore

7. 11/21/2016 7 Evolvable Fuzzy Hardware  Evolution Scheme Training Data Pattern Prediction Search for a good but not optimal rule set The baseline is the working rule set If no better chromosome can be found within the fixed generations, the working fuzzy rule set is deemed to be good enough Core rule set Nanyang Technological University, Singapore

8. 11/21/2016 8 Evolvable Fuzzy Hardware  RFIC (Reconfigurable Fuzzy Inference Chip) Nanyang Technological University, Singapore

9. 11/21/2016 9 ATM Cell-Scheduling Problem  Problem Description Class1 Class2 The capacity of Channels Class1 BUF1 Class2 OUTBR MP BUF2 Nanyang Technological University, Singapore

10. 11/21/2016 10 ATM Cell-Scheduling Problem  Quality of Service Class1 Cell Delay Cell Loss (Class1 and Class2) Balance of Cell Losses (Fairness) Nanyang Technological University, Singapore

11. 11/21/2016 11 ATM Cell-Scheduling Problem ---Available Schemes  FIFO  DWPS  Other Methods Round Robin Scheduling Generalized Processor Sharing Nanyang Technological University, Singapore

12. 11/21/2016 12 EFH Application on Cell-Scheduling Training buffer1 (TB1) Training buffer2 (TB2) Class2 Class1 MP BUF1 BUF2 RFIC Scheduling Model Evolution Module Nanyang Technological University, Singapore

13. 11/21/2016 13 EFH application on Cell-Scheduling  Training Data Principle of “Locality” Using Past Period Cell Flow to Train EFH Search for a good but not optimal rule set. The baseline is the working rule set If no better chromosome can be found within the fixed generations, the working fuzzy rule set is deemed to be good enough. Nanyang Technological University, Singapore

14. 11/21/2016 14 EFH application on Cell-Scheduling  Fuzzy Variables C1=V1/Vmax C2=L2/Lmax  Membership Functions Nanyang Technological University, Singapore

15. 11/21/2016 15 EFH application on Cell-Scheduling  Core Rule Set To Prevent From Adopting Very Poor Rule Set C 1 C 2 VSSMLVL VSTFFFF STTTFF MTTTFF LTTTTF VLTTTTT Nanyang Technological University, Singapore

16. 11/21/2016 16 EFH application on Cell-Scheduling  Coding Scheme 12222,11122,11122,11112,11111  Fitness Function Nanyang Technological University, Singapore

17. 11/21/2016 17 EFH application on Cell-Scheduling  GA Parameters Generation Number=9 Evolution Cycle=2 Population Size=10 Elite Pool Size=2 Crossover Probability=0.6 Mutation Probability=0.05 Nanyang Technological University, Singapore

18. 11/21/2016 18 EFH application on Cell-Scheduling  Simulation Scenario1 CBR is 155.52MHz VBR is 155.52MHz 2 Seconds Scenario2 CBR is 100MHz VBR is from 55.52MHz to 155.52MHz 2 Seconds Nanyang Technological University, Singapore

19. 11/21/2016 19 Simulation Results  Scenario1 Nanyang Technological University, Singapore

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22. 11/21/2016 22 Simulation Results  Scenario2 Nanyang Technological University, Singapore

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24. 11/21/2016 24 Nanyang Technological University, Singapore

25. 11/21/2016 25 Simulation Results  QoS Tunability of EFH Adjusting QoS by adjusting the value of λ. The smaller the λ, the smaller the class1 delay and vice visa. The value of λ can be decided if the desired class1 delay is decided. Nanyang Technological University, Singapore

26. 11/21/2016 26 Simulation Results  Tunability Simulation Nanyang Technological University, Singapore

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30. 11/21/2016 30 Nanyang Technological University, Singapore

31. 11/21/2016 31 Conclusions  The proposed EFH can be successfully applied on ATM cell scheduling  EFH can realize Intrinsic Evolution and Online adaptation.  It can trace the flow pattern and evolve an appropriate rule set.  It can achieve good QoS balance.  The achieved QoS can be adjusted conveniently Nanyang Technological University, Singapore

32. 11/21/2016 32 Conclusions  (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions.  (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered.  (G) The result solves a problem of indisputable difficulty in its field. Nanyang Technological University, Singapore