Genetic Algorithms and their use in
the design of Evolvable Hardware.
     Abhishek Joglekar & Manas Tungare.
Concept of Genetic Algorithms
  Modeled on Biological Evolution
w
w Idea of Optimization in Nature


    Nature's method t...
Biological Background
  Survival of the Fittest -Charles Darwin
w
w The best genes are transferred to the
  next generatio...
Development of a GA:
Simple_Genetic_Algorithm()
      {
      Initialize the Population;
      Calculate Fitness Function;...
Steps in a Genetic Algorithm:
  Population
w
w Selection
w Encoding
w Crossover
w Mutation
w Elitism
w Offspring
Examples:
    Genetic Algorithm examples in Java
w


  The working : A simple applet
w
w The mechanics : Minimization of a...
Evolvable Hardware
  Autonomous Adaptation
w
w On-line Adaptation
w Un-supervised Learning
w Characteristics of Problem no...
Why GA’s for EHW ?
    Why GA’s are an effective solution to
w
    developing Evolvable Hardware
  Configuration Bits = Ch...
Why Evolvable Hardware ?
    The utility and importance of Evolvable
w
    Hardware in …
  Data Compression Hardware
w
w A...
The GRD Chip:
A practical implementation
  Genetic Reconfiguration of DSP’s.
w
w In April 1999, by Japanese Scientists.
w ...
Applications of the GRD Chip
  Dynamic Reconfiguration of DSPs
w
w Embedded Systems in practical
  Industrial Applications...
Scope & Limitations:
  Excellent Solution, but not for all
w
  problems
w Vast Search Space
w Embryonic Circuits (LC, RC, ...
Concluding Remarks:
  A promising area
w
w Although a rich set of methods is
  available, a lot of options are open to
  r...
Questions ?




              ?
Reference:
    This paper is available online at:
w

    http://www.manastungare.com/articles/


w Our     sincere thanks ...
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Genetic Algorithms and their use in the Design of Evolvable Hardware

  1. 1. Genetic Algorithms and their use in the design of Evolvable Hardware. Abhishek Joglekar & Manas Tungare.
  2. 2. Concept of Genetic Algorithms Modeled on Biological Evolution w w Idea of Optimization in Nature Nature's method to evolve optimal w solutions without the hindrance of preconceived knowledge. - Prof. John Koza
  3. 3. Biological Background Survival of the Fittest -Charles Darwin w w The best genes are transferred to the next generation. w The Reproduction Process w Fitness of an Individual.
  4. 4. Development of a GA: Simple_Genetic_Algorithm() { Initialize the Population; Calculate Fitness Function; While(Fitness Value != Optimal Value) { Selection; Crossover; Mutation; Calculate Fitness Function; } }
  5. 5. Steps in a Genetic Algorithm: Population w w Selection w Encoding w Crossover w Mutation w Elitism w Offspring
  6. 6. Examples: Genetic Algorithm examples in Java w The working : A simple applet w w The mechanics : Minimization of a Function
  7. 7. Evolvable Hardware Autonomous Adaptation w w On-line Adaptation w Un-supervised Learning w Characteristics of Problem not known in advance. w “Intelligent” Machines
  8. 8. Why GA’s for EHW ? Why GA’s are an effective solution to w developing Evolvable Hardware Configuration Bits = Chromosomes w w Fitness Function = Performance w No knowledge of search-space required w Continuous Reconfiguration
  9. 9. Why Evolvable Hardware ? The utility and importance of Evolvable w Hardware in … Data Compression Hardware w w Artificial Neural Networks w Ontogenic Neural Networks
  10. 10. The GRD Chip: A practical implementation Genetic Reconfiguration of DSP’s. w w In April 1999, by Japanese Scientists. w 32-bit RISC Processor @ 100 MHz (NEC V830). w Binary Tree Network of 15 16-bit DSP’s @ 33 MHz.
  11. 11. Applications of the GRD Chip Dynamic Reconfiguration of DSPs w w Embedded Systems in practical Industrial Applications. w Ontogenic Neural Networks w Adaptive Equalization
  12. 12. Scope & Limitations: Excellent Solution, but not for all w problems w Vast Search Space w Embryonic Circuits (LC, RC, etc)
  13. 13. Concluding Remarks: A promising area w w Although a rich set of methods is available, a lot of options are open to research. w Scope for further research & development are immense
  14. 14. Questions ? ?
  15. 15. Reference: This paper is available online at: w http://www.manastungare.com/articles/ w Our sincere thanks to: Prof. Sasi Kumar, NCST.

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