1. Ontogenetic systems Drawing inspiration from growth and healing processes of living organisms… …and applying them to electronic computing systems Phylogeny (P) [Evolvability] Epigenesis (E) [Adaptability] Ontogeny (O) [Scalability] PO hw POE hw OE hw PE hw

2. Introduction Motivations for growth-based approaches : Tackling complexity through scalability

3. Introduction Motivations for growth-based approaches : Tackling complexity through scalability Several “theoretical” approaches: L-Systems “Blob” computing Morphogenesis Neuronal growth Few practical approaches in electronics

4. Introduction Motivations for growth-based approaches : Tackling complexity through scalability Fault tolerance through redundancy

5. Development in hardware Mechanisms inspired by the biological process of growth (and healing) applied to networks of processing elements The goal is NOT to mimic biology (or help biologists) but to solve problems in hardware design The goal is NOT to grow form (morphogenesis), but function! i.e., design systems that use development to execute an application better/more efficiently/with non- standard constraints

6. Let us assume that we want to implement a streaming application (i.e. an application that consists of a chain of discrete operations). For example an audio or video decoder. × Development in hardware – Why? ×+÷≠ FFT +IN DCT OUT

7. Development in hardware – Why? Option 1: software only OK, but (relatively) slow Option 2: hardware – full custom circuit Very fast, but expensive and inflexible (if the algorithm changes, the circuit must be redesigned) Option 3: hardware – dedicated processor Fast, but again if the algorithm changes, it needs to be redesigned together with the compiler, the programming tools, etc. Option 4: hardware – array of processing nodes …

8. Development in hardware – Why? Option 4.1: hardware – array of general-purpose processing nodes Fast, very much in fashion (multi-core, GPU), but very difficult to program and again not very flexible. Option 4.1: hardware – array of custom processing nodes Very fast, but difficult to implement and design ×+÷≠ FFT + × DCT ×+INOUT

9. Development in hardware – Why? FPGAs (of various flavours) are the obvious solution to implement arrays of custom processors But the design process is NOT simple

10. Development in hardware – Why? Step 1: analyze the application and extract the component tasks ×+÷≠ FFT + × DCT ×+÷≠ FFT + × IN DCT OUT

11. Development in hardware – Why? Step 2: as a function of the tasks, design one (or more) custom processors. ×+÷≠ FFT + × DCT ×+÷≠ FFT + × IN DCT OUT

12. Development in hardware – Why? Step 3: program the FPGA to implement an array of processors. ×+÷≠ FFT + × DCT ×+÷≠ FFT + × IN DCT OUT

13. Development in hardware – Why? Step 4: Assign the tasks to the processing nodes and set up the connection network. ×+÷≠ FFT + × DCT ×+÷≠ FFT + × IN DCT OUT × ×+ IN ÷≠ FFT + DCT OUT

14. Development in hardware – Why? Option: array of custom processing nodes Step 1: analyze the application and extract the component tasks Step 2: design the custom processors Step 3: program the FPGA Step 4: assign the tasks to the processors and set up the connection network ← Multi-cellular organization ← Evolutionary process ← Totipotent / stem cells ← Growth (cellular division) ← Growth (cellular differentiation)

15. ×+÷≠ FFT + × DCT Programmable substrate Growth Application self-organizes in a programmable substrate ×+÷≠ FFT + × IN DCT OUT

16. Environmental adaptation Self-organization is hard to justify for silicon! …unless growth and structural adaptation cannot be represented in a genome: they are influenced by environmental variables. Substrate defects Runtime faults Performance parameters

17. Programmable substrate Fault tolerance Faults at fabrication are increasing. Self-organization is back! Online faults are increasing Self-organization is back! Similar mechanisms can be used for development and for self-repair (stem cells + differentiation!). Fault tolerance = environmental adaptation. ×+÷≠+ × DCT FFT ××

18. Environmental adaptation Application self-organizes depending on input stream – structural adaptation ×+÷≠ FFT + × IN DCT OUT FFT2DCT ×+÷≠ FFT + × DCT FFT2DCT FFT DCT

20. Next lectures Lecture 2 – Week 4 (Nov. 1): Cellular automata and self-replication Lecture 3 – Week 4 (Nov. 4): Self-replicating loops and the Tom Thumb algorithm Lecture 4 – Week 5 (Nov. 7): Embryonics Lecture 5 – Week 6 (Nov. 18): Self-replicating electronic circuits Lecture 6 – Week 8 (Nov. 29): Adaptive processor arrays Lecture 7 – Week 8 (Dec. 2): Adaptive processor arrays - continued Lecture 8 – Week 9 (Dec. 6): BioWall demo