1. Instructor: Dr. Phillip Jones
CPRE 583 Reconfigurable Computing Lecture 19: Fri 11/5/2010 (Evolvable Hardware)
Instructor: Dr. Phillip Jones
Reconfigurable Computing Laboratory
Iowa State University
Ames, Iowa, USA

2. Announcements/Reminders
MP3: Extended due date until Monday midnight
Those that finish by Friday (11/5) midnight bonus +1% per day before new deadline
If after Friday midnight, no bonus but no penalty
10% deduction after Monday midnight, and addition -10% each day late
Problem 2 of HW 2 (will now call HW3): released by Sunday midnight, will be due Monday 11/22 midnight.
Turn in weekly project report (tonight midnight)

3. Initial Project Proposal Slides (5-10 slides)
Project team list: Name, Responsibility (who is project leader)
Team size: 3-4 (5 case-by-case)
Project idea
Motivation (why is this interesting, useful)
What will be the end result
High-level picture of final product
High-level Plan
Break project into mile stones
Provide initial schedule: I would initially schedule aggressively to have project complete by Thanksgiving. Issues will pop up to cause the schedule to slip.
System block diagrams
High-level algorithms (if any)
Concerns
Implementation
Conceptual
Research papers related to you project idea

4. Projects Ideas: Relevant conferences
FPL
FPT
FCCM
FPGA
DAC
ICCAD
Reconfig
RTSS
RTAS
ISCA
Micro
Super Computing
HPCA
IPDPS

5. Initial Project Proposal Slides (5-10 slides)
Project team list: Name, Responsibility (who is project leader)
Project idea
Motivation (why is this interesting, useful)
What will be the end result
High-level picture of final product
High-level Plan
Break project into mile stones
Provide initial schedule: I would initially schedule aggressively to have project complete by Thanksgiving. Issues will pop up to cause the schedule to slip.
System block diagrams
High-level algorithms (if any)
Concerns
Implementation
Conceptual
Research papers related to you project idea

6. Weekly Project Updates
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
Your Initial Project proposal presentation (Due Fri 10/22). Should make for a starting point for you Final presentation
What things are work & not working
What roadblocks are you running into

7. Projects: Target Timeline
Teams Formed and Idea: Mon 10/11
Project idea in Power Point 3-5 slides
Motivation (why is this interesting, useful)
What will be the end result
High-level picture of final product
Project team list: Name, Responsibility
High-level Plan/Proposal: Fri 10/22
Power Point 5-10 slides
System block diagrams
High-level algorithms (if any)
Concerns
Implementation
Conceptual
Related research papers (if any)

8. Projects: Target Timeline
Work on projects: 10/ /8
Weekly update reports
More information on updates will be given
Presentations: Last Wed/Fri of class
Present / Demo what is done at this point
15-20 minutes (depends on number of projects)
Final write up and Software/Hardware turned in: Day of final (TBD)

9. Project Grading Breakdown
50% Final Project Demo
30% Final Project Report
30% of your project report grade will come from your 5-6 project updates. Friday’s midnight
20% Final Project Presentation

10. What you should learn Understand Evolvable Hardware basics?
Benefits and Drawbacks
Key types/categories

11. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream

12. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream
GATACA

13. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream
GATACA
GATAGA

14. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream
GATACA
GATAGA

15. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream

16. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream

17. Evolvable Hardware
One of the first papers to compare reconfigurable HW with biological organisms (1993)
“Evolvable Hardware with Genetic Learning: A first step towards building a Darwin Machine”, Higuchi
Biological organism => DNA
GATACAAAGATACACCAGATA
Reconfigurable Hardware => Configuration bitstream
DFF
DFF

18. Classifying Adaption/Evolution
Phylogeny Ontogeny Epigenesis (POE)
Phylogeny: Evolution through recombination and mutations
Biological reproduction : Genetic Algorithms
Ontogeny: Self replication
Multicellular organism's cell division : Cellular Automata
Epigenesis: adaptation trigger by external environment
Immune system development : Artificial Neural Networks

19. Classifying Adaption/Evolution
Phylogeny Ontogeny Epigenesis (POE)
Phylogeny: Evolution through recombination and mutations
Biological reproduction : Genetic Algorithms
Ontogeny: Self replication
Multicellular organism's cell division : Cellular Automata
Epigenesis: adaptation trigger by external environment
Immune system development : Artificial Neural Networks
Phylogeny
Epigenesis
Ontogeny

20. Artificial Evolution
30/40 year old concept. But applying to reconfigurable hardware is newish (1990’s)
Evolutionary Algorithms (EAs)
Genetic Algorithms
Genetic Programming
Evolution Strategies
Evolutionary programming

21. Artificial Evolution
30/40 year old concept. But applying to reconfigurable hardware is newish (1990’s)
Evolutionary Algorithms (EAs)
Genetic Algorithms
Genetic Programming
Evolution Strategies
Evolutionary programming

22. Artificial Evolution
30/40 year old concept. But applying to reconfigurable hardware is newish (1990’s)
Evolutionary Algorithms (EAs)
Genetic Algorithms
Genetic Programming
Evolution Strategies
Evolutionary programming

23. Genetic Algorithms
Genome: a finite sting of symbols encoding an individual
Phenotype: The decoding of the genome to realize the individual
Constant Size population
Generic steps
Initial population
Decode
Evaluate (must define a fitness function)
Selection
Mutation
Cross over

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

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

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

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

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

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

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

31. Evolvable Hardware Platform

32. Genetic Algorithms GA are a type of guided search
Why use a guide search? Why not just do an exhaustive search?

33. Genetic Algorithms GA are a type of guided search
Why use a guide search? Why not just do an exhaustive search?
Assume
1 billion individuals can be evaluated a second
The genome of a individual is 32-bits in size
How long to do an exhaustive search?

34. Genetic Algorithms GA are a type of guided search
Why use a guide search? Why not just do an exhaustive search?
Assume
1 billion individuals can be evaluated a second
Now genome of a individual is a FPGA
1,000,000 bits in size
How long to do an exhaustive search?

35. Evolvable Hardware Taxonomy
Extrinsic Evolution (furthest from biology)
Evolution done in SW, then result realized in HW
Intrinsic Evolution
HW is used to deploy individuals
Results are sent back to SW for fitness calculation
Complete Evolution
Evolution is completely done on target HW device
Open-ended Evolution (closest to biology)
Evaluation criteria changes dynamically
Phylogeny
Epigenesis
Ontogeny

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

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

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

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

40. Evolvable Hardware Issues?

41. Evolvable Hardware Issues?

42. Evolvable Hardware Platforms
Commercial Platforms
Xilinx XC6200
Completely multiplex base, thus could program random bitstreams dynamically without damaging chip
Xilinx Virtex FPGA
Custom Platforms
POEtic cell
Evolvable LSI chip (Higuchi)

43. Next Lecture
Overview the synthesis process

44. Notes
Notes