Evolvable Fuzzy Hardware for Real-time Embedded Control in Packet Switching

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Evolvable Fuzzy Hardware for Real-time Embedded
Control in Packet Switching

• Ju Hui Li
• Meng Hiot Lim
• Qi Cao

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Outline
Nanyang Technological University, Singapore

• Introduction to EHW
• Evolvable Fuzzy Hardware (EFH)
• Hardware Implementation (RFIC)
• ATM Cell-Scheduling
• EFH application on Cell-Scheduling
• Simulation Results
• Conclusion and Future Work

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Introduction to EHW
Nanyang Technological University, Singapore

• Definition
• Modify
• Autonomously
• Classification Methods
• Methods of Evolution
• Extrinsic, Intrinsic
• Adaptation Scheme
• On-line, Off-line
• Evolutionary Granularity
• Transistor, Gate, Function Units

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Introduction to EHW
Nanyang Technological University, Singapore

• Open Issues
• On-line Adaptation
• Scalability
• Termination of Evolution

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Evolvable Fuzzy Hardware
Nanyang Technological University, Singapore

• 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

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Evolvable Fuzzy Hardware
Nanyang Technological University, Singapore

• Architecture

Evaluation
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Evolvable Fuzzy Hardware
Nanyang Technological University, Singapore

• 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

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Evolvable Fuzzy Hardware
Nanyang Technological University, Singapore

• RFIC (Reconfigurable Fuzzy Inference Chip)

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ATM Cell-Scheduling Problem
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• Problem Description
• Class1
• Class2
• The capacity of Channels

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ATM Cell-Scheduling Problem
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• Quality of Service
• Class1 Cell Delay
• Cell Loss (Class1 and Class2)
• Balance of Cell Losses (Fairness)

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ATM Cell-Scheduling Problem ---Available
Schemes
Nanyang Technological University, Singapore

• FIFO
• DWPS
• Other Methods
• Round Robin Scheduling
• Generalized Processor Sharing

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EFH Application on Cell-Scheduling
Nanyang Technological University, Singapore

Training buffer1 (TB1)
Training buffer2 (TB2)
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EFH application on Cell-Scheduling
Nanyang Technological University, Singapore

• 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.

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EFH application on Cell-Scheduling
Nanyang Technological University, Singapore

• Fuzzy Variables
• C1V1/Vmax
• C2L2/Lmax
• Membership Functions

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EFH application on Cell-Scheduling
Nanyang Technological University, Singapore

• Core Rule Set
• To Prevent From Adopting Very Poor Rule Set

C1 C2 VS S M L VL
VS T F F F F
S T T T F F
M T T T F F
L T T T T F
VL T T T T T
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EFH application on Cell-Scheduling
Nanyang Technological University, Singapore

• Coding Scheme
• 12222,11122,11122,11112,11111
• Fitness Function

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EFH application on Cell-Scheduling
Nanyang Technological University, Singapore

• GA Parameters
• Generation Number9
• Evolution Cycle2
• Population Size10
• Elite Pool Size2
• Crossover Probability0.6
• Mutation Probability0.05

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EFH application on Cell-Scheduling
Nanyang Technological University, Singapore

• 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

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Simulation Results
Nanyang Technological University, Singapore

• Scenario1

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Simulation Results
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• Scenario2

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Simulation Results
Nanyang Technological University, Singapore

• QoS Tunability of EFH
• Adjusting QoS by adjusting the value of ?.
• The smaller the ?, the smaller the class1 delay
  and vice visa.
• The value of ? can be decided if the desired
  class1 delay is decided.

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Simulation Results
Nanyang Technological University, Singapore

• Tunability Simulation

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Conclusions
Nanyang Technological University, Singapore

• 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

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Conclusions
Nanyang Technological University, Singapore

• (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.