Bio-inspiration-1/PMa csem P. Marchal Centre Suisse d'Electronique et de Microtechnique SA Jaquet-Droz 1 CH-2007 Neuchâtel Embryological Electronics First NASA/DoD Workshop on Evolvable Hardware

Bio-inspiration-2/PMa csem Summary Introduction to Bio-inspired Systems Embryological Electronics What is presently available ? Open Avenues for Evolvable Hardware Conclusion

Bio-inspiration-3/PMa csem Introduction to Bio-inspired Systems What is bio-inspiration? Building complex systems Genome-based design

Bio-inspiration-4/PMa csem 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

Bio-inspiration-5/PMa csem 3-billion years shrinked into 1 year January, the 1 st Earth formation March, the 1 st Sedimentary rocks May, the 1 st First cells : prokaryotes July, the 1 st Free oxygen in the air September, the 1 st Eukaryotes: differentiated nucleus November, the 19 th Cambrian explosion: fossil era December, the 26 th Death of dynosaurs December, the 31 st At 9:00 pm Homo erectus At 11:45 pm Homo sapiens At 12:00 pm You January, the 1 st Y 2 K bug

Bio-inspiration-6/PMa csem This is NOT bio-inspiration

Bio-inspiration-7/PMa csem Fields of Bio-inspiration optics Artificial life mechanics sensors actuators self- structuration perception Neural nets Neural nets perceptron algorithms Genetic algo healing evolution VLSI

Bio-inspiration-8/PMa csem Building Complex Systems 1.- Engineer’s approach 

Bio-inspiration-9/PMa csem Building Complex Systems 2.- Nature’s Approach (1) 0.1mm fertilized egg 1/2 hour, 1 cell 3 hours, 64 cells 6 hours, 10'000 cells

Bio-inspiration-10/PMa csem Building Complex Systems 2.- Nature’s Approach (2) NERVE CELL MUSCLE CELL LEUCOCYTE LYMPHOCYTES OSTEOCYTE SPERMATOZOON RED CELLS FIBROPLAST 10 hours, 30'000 cells

Bio-inspiration-11/PMa csem Field Programmable Gate Arrays Field Programme Functional Part Interconnection Part Horizontal Buses Vertical Buses

Bio-inspiration-12/PMa csem 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

Bio-inspiration-13/PMa csem Self-structuring VLSI (genome-based design)

Bio-inspiration-14/PMa csem Biodule (biological-like module)

Bio-inspiration-15/PMa csem Embryological Electronics Reproduction Adaptation Evolution

Bio-inspiration-16/PMa csem No reproduction apparatus

Bio-inspiration-17/PMa csem A cell composed of proto-cells The silicon cell is composed of:  Genome memory  Address computation  Functional cell  Failure handling

Bio-inspiration-18/PMa csem 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

Bio-inspiration-19/PMa csem Storing Process.

Bio-inspiration-20/PMa csem Each Nucleus stores its own copy = = = = = = = = 

Bio-inspiration-21/PMa csem Gradient-like control proto-cell Its function is to:  compute the local address (row & column coordinates)  transmit a copy of the local address to the neighbouring cells  boot the differentiation process (gene expression)

Bio-inspiration-22/PMa csem Local Address Computation 0,1

Bio-inspiration-23/PMa csem 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 4,45,4

Bio-inspiration-24/PMa csem Repeating Structures 0,1  1,1 2,1 1,2 1,1 2,2 1,3 2,1 1,2 2,3 1,1 2,2 1,3 2,1 1,2 2,3 1,1 1,3 2,11,1

Bio-inspiration-25/PMa csem 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

Bio-inspiration-26/PMa csem Differentiation Process.

Bio-inspiration-27/PMa csem Functional Cell Its function is to:  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

Bio-inspiration-28/PMa csem Family of Cells D Q Q Reset Set Clock FUNCTIONAL PART INTER CONNECTION PART FIELD PROGRAMME LOCAL GENE

Bio-inspiration-29/PMa csem Immune-like Proto-Cell Its function is to:  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

Bio-inspiration-30/PMa csem Healing Process X Y

Bio-inspiration-31/PMa csem Healing Process

Bio-inspiration-32/PMa csem What is presently available ? A family of self-structuring circuits

Bio-inspiration-33/PMa csem A family of self-structuring circuits MUXTREE (EPFL - 94)  BIODULE 600 DMUXTREE (CSEM - 95)  S.T. HCMOS5.5  m GenomIC (CSEM 96)  MIETEC HCMOS7.75  m MICTREE (EPFL - 97)  BIODULE 602 SRMUX (EPFL - 98)  BIODULE 603 FPOP (CSEM - 98)   EM Marin SOI 1  m FPPA (CSEM - 99)  TSMC.35  m FrameDISC (CSEM - 00)  TSMC.25  m Medium Low High CELL COMPLEXITY

Bio-inspiration-34/PMa csem DMUXTREE

Bio-inspiration-35/PMa csem GenomIC

Bio-inspiration-36/PMa csem Field Programmable Processor Array (FPPA)

Bio-inspiration-37/PMa csem Open Avenues for Evolvable Hardware Applications Adaptation Evolution

Bio-inspiration-38/PMa csem 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)

Bio-inspiration-39/PMa csem 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

Bio-inspiration-40/PMa csem 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

Bio-inspiration-41/PMa csem Conlusion

Bio-inspiration-42/PMa csem Parallelism, morphism and adaptation Massive parallelism: –Multicellular organization Morphism: –Configurable hardware Adaptation: –Upgradable software –Reconfigurable hardware

Bio-inspiration-43/PMa csem 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

Bio-inspiration-44/PMa csem