1. Instructor: Dr. Phillip Jones
CPRE 583 Reconfigurable Computing Lecture 9: Fri 11/6/2009 (Evolvable Hardware)
Instructor: Dr. Phillip Jones
Reconfigurable Computing Laboratory
Iowa State University
Ames, Iowa, USA

2. Overview
Class Projects
Finish Evolvable Hardware (Chapter 33)

3. Project Grading Breakdown
60% Final Project Demo
30% Final Project Report
30% of your project report grade will come from your 5 project updates. Friday’s midnight
10% Final Project Presentation

4. Project Update The current state of your project write up
Even in the early stages of the project you should be able to write a rough draft of the Introduction and Motivation section
The current state of your Final Presentation
What things are work & not working
What roadblocks are you running into

5. What you should learn
What are the core categories of evolvable hardware?

6. Evolvable Hardware Intro to Genetic Algorithms
Evolvable Hardware Taxonomy
Extrinsic Evolution
Intrinsic Evolution
Complete Evolution
Open-ended Evolution

7. Genetic Algorithms
Genome: a finite sting of symbols encoding the chrematistics of an individual
Phenotype: The decoding of the genome to realize the individual

8. Initialize Population
Genetic Algorithms
Initialize Population
Evaluate
Decode
Next Generation
Selection
Cross
Over
Mutation

9. Initialize Population
Genetic Algorithms
Initialize Population
Evaluate
Decode
( )
Next Generation
Selection
Cross
Over
Mutation

10. Initialize Population
Genetic Algorithms
Initialize Population
(.40)
(.70)
(.20)
(.10)
(.10)
(.60)
Evaluate
Decode
( )
Next Generation
Selection
Cross
Over
Mutation

11. Initialize Population
Genetic Algorithms
Initialize Population
(.40)
(.70)
(.20)
(.10)
(.10)
(.60)
Evaluate
Decode
( )
Next Generation
Selection
Cross
Over
(.40)
(.70)
(.60)
Mutation

12. Initialize Population
Genetic Algorithms
Initialize Population
(.40)
(.70)
(.20)
(.10)
(.10)
(.60)
Evaluate
Decode
( )
Next Generation
Selection
Cross
Over
(.40)
(.70)
(.60)
Mutation

13. Initialize Population
Genetic Algorithms
Initialize Population
(.40)
(.70)
(.20)
(.10)
(.10)
(.60)
Evaluate
Decode
( )
Next Generation
Selection
Cross
Over
(.40)
(.70)
(.60)
Mutation

14. Initialize Population
Genetic Algorithms
Initialize Population
(.40)
(.70)
(.20)
(.10)
(.10)
(.60)
Evaluate
Decode
( )
Next Generation
Selection
Cross
Over
(.40)
(.70)
(.60)
Mutation

15. Evolvable Hardware Taxonomy
Extrinsic Evolution
Evolution done in SW, then result realized in HW
Intrinsic Evolution
HW is used for computing fitness function
Complete Evolution
Evolution is completely done on target HW device
Open-ended Evolution
Evaluation criteria changes dynamically

16. Evolvable Hardware Applications
Prosthetic Hand controller chip
Kajitani “An Evolvable Hardware Chip for Prostatic Hand Controller”, 1999

17. Evolvable Hardware Applications

18. Evolvable Hardware Applications
Tone Discrimination and Frequency generation
Adrian Thompson “Silicon Evolution”, 1996
Xilinx XC6200

19. Evolvable Hardware Applications
Tone Discrimination and Frequency generation
Node Functions
Node Genotype

20. Evolvable Hardware Applications
Tone Discrimination and Frequency generation
Evolved 4KHz oscillator

21. Next Lecture
Streaming Application Chapter 8 & 9

22. Slides in Progress
Need to revise this lecture with figures, and useful animations
Add some non-FPGA systems, maybe not since GARP, and PipeRench were discussed in last lecture. Perhaps just mention again
Main reason other archs are not used is economy of scales. Lots of FPGAs are manufacture, thus lowing cost and enable the use of state of the art fab technology (given high performance