Instructor: Dr. Phillip Jones CPRE 583 Reconfigurable Computing Lecture 19: Fri 11/5/2010 (Evolvable Hardware) Instructor: Dr. Phillip Jones (phjones@iastate.edu) Reconfigurable Computing Laboratory Iowa State University Ames, Iowa, USA http://class.ee.iastate.edu/cpre583/

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)

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

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

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

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

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)

Projects: Target Timeline Work on projects: 10/22 - 12/8 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)

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

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

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 000100111011011011011010101

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 000100111011011011011010101 GATACA

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 000100111011011011011010101 GATACA GATAGA

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 000100111011011011011010101 GATACA GATAGA

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 000100111011011011011010101 0001 1100

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 000100111011011011011010101 0001 1100 0001 0000

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 000100111011011011011010101 DFF 0001 1100 0001 0000 DFF

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

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

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

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

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

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

Initialize Population Genetic Algorithms Initialize Population 0000 1000 1010 1111 1100 0000 1111 0000 0000 0000 1111 1111 Evaluate Decode Next Generation Selection Cross Over Mutation

Initialize Population Genetic Algorithms Initialize Population 0000 1000 1010 1111 1100 0000 1111 0000 0000 0000 1111 1111 1010 0011 1010 0100 1100 0000 1111 0000 0000 0000 1111 0100 Evaluate Decode (0110 1000) Next Generation Selection Cross Over Mutation

Initialize Population Genetic Algorithms Initialize Population 0000 1000 1010 1111 1100 0000 1111 0000 0000 0000 1111 1111 1010 0011 1010 0100 1100 0000 1111 0000 0000 0000 1111 0100 1010 0011 (.40) 1010 0100 (.70) 1100 0000 (.20) 1111 0000 (.10) 0000 0000 (.10) 1111 0100 (.60) Evaluate Decode (0110 1000) Next Generation Selection Cross Over Mutation

Initialize Population Genetic Algorithms Initialize Population 0000 1000 1010 1111 1100 0000 1111 0000 0000 0000 1111 1111 1010 0011 1010 0100 1100 0000 1111 0000 0000 0000 1111 0100 1010 0011 (.40) 1010 0100 (.70) 1100 0000 (.20) 1111 0000 (.10) 0000 0000 (.10) 1111 0100 (.60) Evaluate Decode (0110 1000) Next Generation Selection Cross Over 1010 0011 (.40) 1010 0100 (.70) 1111 0100 (.60) Mutation

Initialize Population Genetic Algorithms Initialize Population 0000 1000 1010 1111 1100 0000 1111 0000 0000 0000 1111 1111 1010 0011 1010 0100 1100 0000 1111 0000 0000 0000 1111 0100 1010 0011 (.40) 1010 0100 (.70) 1100 0000 (.20) 1111 0000 (.10) 0000 0000 (.10) 1111 0100 (.60) Evaluate Decode (0110 1000) Next Generation 1110 0011 1010 0000 1110 0100 1010 1011 0010 0000 1111 0100 Selection Cross Over 1010 0011 (.40) 1010 0100 (.70) 1111 0100 (.60) Mutation

Initialize Population Genetic Algorithms Initialize Population 0000 1000 1010 1111 1100 0000 1111 0000 0000 0000 1111 1111 1010 0011 1010 0100 1100 0000 1111 0000 0000 0000 1111 0100 1010 0011 (.40) 1010 0100 (.70) 1100 0000 (.20) 1111 0000 (.10) 0000 0000 (.10) 1111 0100 (.60) Evaluate Decode (0110 1000) Next Generation 1110 0000 1010 0011 1110 0100 1010 0000 0010 1011 1111 0100 1110 0011 1010 0000 1110 0100 1010 1011 0010 0000 1111 0100 Selection Cross Over 1010 0011 (.40) 1010 0100 (.70) 1111 0100 (.60) Mutation

Initialize Population Genetic Algorithms Initialize Population 1110 0000 1010 0011 1110 0100 1010 0000 0010 1011 1111 0100 1010 0011 1010 0100 1100 0000 1111 0000 0000 0000 1111 0100 1010 0011 (.40) 1010 0100 (.70) 1100 0000 (.20) 1111 0000 (.10) 0000 0000 (.10) 1111 0100 (.60) Evaluate Decode (0110 1000) Next Generation 1110 0000 1010 0011 1110 0100 1010 0000 0010 1011 1111 0100 1110 0011 1010 0000 1110 0100 1010 1011 0010 0000 1111 0100 Selection Cross Over 1010 0011 (.40) 1010 0100 (.70) 1111 0100 (.60) Mutation

Evolvable Hardware Platform

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

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?

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?

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

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

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

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

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

Evolvable Hardware Issues?

Evolvable Hardware Issues?

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)

Next Lecture Overview the synthesis process

Notes Notes