Embryological Electronics First NASA/DoD Workshop on Evolvable Hardware

Bio-inspiration-1/PMa - 07.99

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P. Marchal

Centre Suisse d'Electronique et de Microtechnique SA

Jaquet-Droz 1

CH-2007 Neuchâtel

[email protected]

http://www.csem.ch

Embryological Electronics

First NASA/DoD Workshop onEvolvable Hardware


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Summary

• Introduction to Bio-inspired Systems

• Embryological Electronics

• What is presently available ?

• Open Avenues for Evolvable Hardware

• Conclusion


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Introduction to Bio-inspired Systems

What is bio-inspiration?

Building complex systems

Genome-based design


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Bio-inspiration?

Nature has acquired a strong experience in complex system design :

3-billion years of R &D

Powerful constructions (built and maintained) :

longer than hundreds years (animal life)

longer than thousands years (plant life)

Adapting and Evolving solutions:

personal modification is adaptation or learning

inherited modification is evolution


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3-billion years shrinked into 1 year

January, the 1st Earth formation

March, the 1st Sedimentary rocks

May, the 1st First cells : prokaryotes

July, the 1st Free oxygen in the air

September, the 1st Eukaryotes: differentiated nucleus

November, the 19th Cambrian explosion: fossil era

December, the 26th Death of dynosaurs

December, the 31st

At 9:00 pm Homo erectusAt 11:45 pm Homo sapiensAt 12:00 pm You

January, the 1st Y 2 K bug


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This is NOT bio-inspiration


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Fields of Bio-inspiration

opticsArtificial life

mechanics

sensors

actuatorsself-

structuration

perc

eptio

n

NeuralnetsNeural nets

perceptron

algorithms

Genetic algo

healing evolution

VLSI


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Building Complex Systems 1.- Engineer’s approach


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Building Complex Systems2.- Nature’s Approach (1)

0.1mm

fertilizedegg

1/2 hour, 1 cell 3 hours, 64 cells 6 hours, 10'000 cells


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Building Complex Systems2.- Nature’s Approach (2)

NERVE CELL

MUSCLE CELL

LEUCOCYTE

LYMPHOCYTES

OSTEOCYTE

SPERMATOZOON

RED CELLS

FIBROPLAST 10 hours, 30'000 cells


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Field Programmable Gate Arrays

FieldProgramme

FunctionalPart

Interconnection Part

HorizontalBuses

VerticalBuses


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Von Neumann Contribution

• He proposed that the production of an automaton by another one should be composed of two phases:

– information is once read and copied (transcription)

– information is then read and interpreted (translation)

• He conceived a self-reproducing automaton


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Self-structuring VLSI(genome-based design)


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Biodule (biological-like module)


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Embryological Electronics

Reproduction

Adaptation

Evolution


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No reproduction apparatus


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A cell composed of proto-cells

• The silicon cell is composed of:

Genome memory

Address computation

Functional cell

Failure handling


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Nucleus-like proto-cell

• Its function is to:

store the genogram (set of bit-strings - “genes” - that describes the functionality of the silicon cell)

transmit a copy of the genogram to neighbouring cells

boot the address computation


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Storing Process

23306=00000=22302=32040=32000=22000=01001=10001=01050


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Each Nucleus stores its own copy

23306=00000=22302=32040=32000=22000=01001=10001=01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050


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Gradient-like control proto-cell


compute the local address (row & column coordinates)

transmit a copy of the local address to the neighbouring cells

boot the differentiation process (gene expression)


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Local Address Computation

0,1


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Continuous Gradient

0,1 1,1 2,1

1,2

3,1

2,2

1,3

4,1

3,2

2,3

1,4

5,1

4,2

3,3

2,4

5,2

4,3

3,4

5,3

4,4 5,4


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Repeating Structures

0,1 1,1 2,1

1,2

1,1

2,2

1,3

2,1

1,2

2,3

1,1

1,1

2,2

1,3

2,1

1,2

2,3

1,1

1,3

2,1 1,1


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Cell Differentiation

• the local address is used to pick up, out of the genogram memory, the gene corresponding to that location

• the gradient like process enables cell differentiation


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Differentiation Process

1,1 2,1 3,1 1,1 2,1

1,2 2,2 3,2 1,2 2,2

1,3 2,3 3,3 1,3 2,3

1,1 2,1 3,1 1,1 2,123306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050

23306 00000 2230232040 32000 2200001001 10001 01050


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Functional Cell


load the programmable bit-string of the FPGA proto-cell with the local gene

realise a part of the logical function (distributed among the circuit area)

transmit convenient information with the appropriate neighbours


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Family of Cells

D Q

Q

Reset

Set

Clock

FUNCTIONALPART

INTERCONNECTION

PART

FIELD PROGRAMMELOCAL GENE


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Immune-like Proto-Cell


determine the faulty behaviour of a cell, if any, and the severity of the fault

transmit the internal state (faulty or not) to the neighbours

boot the healing phase (restart address computation) if a fault has occurred


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Healing Process

332 0 6 000 0 0 322 02

023 4 0 023 0 0 022 00

010 0 1 001 0 1 010 50

11 21 31

12 22 32

13 23 33

1 2 3

1

2

3

X

Y

332 0 6 000 0 0 322 02

023 4 0 023 0 0 022 00

010 0 1 001 0 1 010 50

11 21 31

12 22 32

13 23 33

4 5 6


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Healing Process

332 0 6 000 0 0

023 4 0

010 0 1 001 0 1

11 21

12 22

13 23

1 2 3

1

2

3

X

Y

332 0 6

023 4 0

010 0 1 001 0 1

11 21

32 12 22

33 13 23

4 5 6

322 02 000 0 0

023 0 0 023 0 0022 00

010 50

31


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What is presently available ?

A family of self-structuring circuits


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A family of self-structuring circuits

MUXTREE (EPFL - 94) BIODULE 600

DMUXTREE (CSEM - 95) S.T. HCMOS5 .5m

GenomIC (CSEM 96) MIETEC HCMOS7 .75m

MICTREE (EPFL - 97) BIODULE 602

SRMUX (EPFL - 98) BIODULE 603

FPOP (CSEM - 98) EM Marin SOI 1m

FPPA (CSEM - 99) TSMC .35m

FrameDISC (CSEM - 00) TSMC .25m

Medium

Low

High

CE

LL

CO

MP

LE

XIT

Y


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DMUXTREE


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GenomIC


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Field Programmable Processor Array (FPPA)


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Open Avenues for Evolvable Hardware

Applications

Adaptation

Evolution


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Applications

• Self-structuring and self-repairing VLSI should be considered in situations where changing and/or repairing is:

– too difficult (under sea exploration)

– too dangerous (nuclear exposition)

– too expensive (deep space exploration)

– too risky (human life is in danger)

• and functionality should be conserved in presence of defects, radiations or wear out

• Emerging applications in automotive (WINS project)


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Adaptation

• Reconfiguration is based on an event differing from the occurrence of a fault

• Physical event adaptation:

– swing of power lines

– shift in temperature

• Informational event adaptation:

– change of signal’s bandwidth

– object oriented processing


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Evolution

• Development is based on a description of the structure stored in a genome

• Use the genetic algorithm and genetic programming techniques to evolve such systems

• Two levels of description may be considered:

– high level description evolution for synthesis

– low level description evolution for adaptation


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Conlusion


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Parallelism, morphism and adaptation

• Massive parallelism:

– Multicellular organization

• Morphism:

– Configurable hardware

• Adaptation:

– Upgradable software

– Reconfigurable hardware


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To conclude

• We have investigated this research domain

• We have acquired the know-how to address a large amount of questions related to fault tolerance as well as evolvable hardware

• We have the mastery of the technology

• We have patents on it

• We are ready to answer any question regarding this field


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Embryological Electronics First NASA/DoD Workshop on Evolvable Hardware

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