1 Evolution on JPL SABLES (Stand-Alone Board-Level Evolvable System ) A platform for fast on-chip evolutionary experiments that is essential for scaling-up the complexity of evolved HW systems. Motivation: Develop a Evolvable Hardware systems that is compact (handheld) low-power fast (seconds for complete evolution) autonomous.

2 Structure of SABLES Reconfiguration mechanism (evolutionary algorithm): TI DSP; Reconfigurable Hardware: Field Programmable Transistor Array (FPTA2) Performance: 1-2 orders of magnitude reduction in memory, 4+ orders of magnitude improvement in speed compared to systems evolving in simulations. 8”x8”x3”

3 SABLE details FPTA – fast evaluation compared to simulation of SPICE netlist DSP + FPTA –Fast download for evaluation of individuals –Good architecture for moving to a self-reconfigurable system-on-a-chip Fault-tolerant solution on a chip Sensors, actuators

4 SABLE architecture

5 Main System USB – JTAG emulator –Is used for downloading the EA to the DSP (2 seconds to download) –Can be used for interaction with EA via built-in GEL functions Slider bars for changing variables Graphs for displaying array contents Works in real-time Innovative Integration Single Board Computer (DSP) –160MHz, 1MB on-chip SRAM, 16MB off-chip SRAM, 32 bits of DIO –Evaluate 1000 individuals per second –Download one cell in 50us, entire configuration in 3mS –DAC40, 40MHz 4-channel 14-bit DAC with 8kB ring-buffer –AIX20, 20MHz 4-channel 16-bit ADC

6 Parameters 64 cells * 5 WORDs = 1.3kB per config Population size can be as large as 10,000, stored in off- chip SRAM Memory –On-chip SRAM (1 clock-cycle delay) –Off-chip SRAM (2 clock-cycle delay) –Implication – use on-chip memory for input waveform manipulation

7 FPTA Details Chip Architecture Cell Schematic Technology: TSMC 0.18u Area 5mm x 7mm; 64 cells; Total of 256 pins; 96 analog/digital inputs 64 analog/digital outputs; 16 bits data bus/9 bits address bus control logic; About 5000 programming bits;

8 FPTA2 - Tested Circuits Gaussian OpAmps In Out

DSP Innovative Integration SBC167stand-alone DSP board; TMS320C6701processor 16 analog inputs and outputs at 100 kSamples; 32 Digital I/O at 7.5MHz. Fits in a box 8” x 8” x 3”.

NIECE – Novel Interface for an Evolutionary Computing Environment Features: Fully graphical user interface Ability to control all evolutionary parameters Java/C Hybrid – fully portable, optimized for speed Compressed storage of an entire evolutionary experiment offline Batch Mode functionality Several Data Views Compatible with an evolutionary systems with infinite generations, population size, inputs, responses, chromosome size USB Interface with SABLE Overview: Graphical User Interface for Visualization of Evolutionary Experiments Goals: Provide a customizable data acquisition protocol to selectively collect data View evolution in several ways, online and offline Save data in a compressed format Interface with any evolutionary system

11 Graphical Interface for Evolutionary Design Specify All GA Parameters at runtime Several possible acquisition protocols Collect data only from user specified generations Collect only user specified individuals (from and to specified ranks, with a stride) Collect only user specified data vectors Parameters can be different per generation and changed on the fly User specified fitness function for compilation GA Parameters Data Acquisition

12 Graphical Interface for Evolutionary Design Response View over several generationsResponse view per generation Chromosome View Easily compare chromosomes for similarities and patterns through population and generations (3 best individuals over 6 generations) Step through a previously completed evolution to recreate the experiment Compare Selected Individuals View Multiple Responses Gen0 Gen1 Gen2 Gen3 Gen4 Gen5

Data Acquisition From SABLE USB Protocol provides rapid data acquisition (300KBps) SABLE USB NIECE Command Packets Transmit all GA parameters (mutation, crossover, etc) 32byte packets Communicate customized acquisition settings – significantly reduces debugging and analysis time Change & control evolution on the fly Data Packets Send back compressed data 64byte packets Configuration bitsGA ParametersAcquisition Parameters 32 bytes Configuration bitsFitness valuesChromosomesResponsesOther Data 64 bytes

14 Evolution on SABLES Evolutionary Algorithm Assess fitness Specs. Chromosomes Configuration bitstrings Responses Candidate configurations are tested on-chip; the best ones are modified by the evolutionary algorithm in a guided search for solutions. Evolutionary Processor (EP) in DSP Reconfigurable Hardware Field Prog.Transistor Array (FPTA) FPTAEP (DSP) Information flow and implementation with DSP/FPTA chip pair SABLES Speed. Portability. Stand-alone. Autonomy?

15 Evolution on SABLES (Half-wave rectifier) Stimulus-response waveforms during the evaluation of a population in one generation (left) and for 2 individuals in the population (right) Excitation input of 2kHz sine wave of amplitude 2V; 20-second experiment 9% elite percentage; 70% crossover; 4% mutation;100 individuals population;

16 Half-wave rectifier evolution waveforms Solution at generation 82 (after 8200 circuits evaluated in ~8s) Best individual of generation a) 1, b) 5, c) 50 d) 82

17 Half-wave rectifier fitness progress The fitness function as generations progress.

18 Self-configuration for functional change When a function is specified the hardware evolves/self- configures to a circuit satisfying the requirements; when mission/requirements change, the same hardware changes again subject to a new evolutionary search Analog multiplier Requirement ARequirement B Digital multiplexer Percentage of satisfaction of requirement 100% 0% Function 1Function 2 time

19 Other evolved circuits 3-bit DAC 4-bit DAC Automatic Gain Control response Oscillators In2 Out1 Out2 In1 Signal SeparatorsHigh-Pass Filters In Out In Out In

20 Evolution on SABLES (Tunable Filters) Tunable filter circuit circuit Excitation input: –e1 = Asin(2  f1) + Bsin (2  f2) –A and B unknown Objective: Amplify more powerful frequency component, attenuate the weakest (pass A if A>B, else pass B). 9% elite percentage; 70% crossover; 4% mutation; 400 individuals population; 60 seconds experiments

21 Evolution on SABLES (Tunable Filters ) Filter characteristic: 10kHz : -2.86dB attenuation 20kHz : -15.8dB attenuation Filter characteristic: 10kHz : -12.5dB attenuation 20kHz : -4.8dB attenuation Input Output Input Output Signal/Noise = 4.1dB Signal/Noise = 16.9dB Signal/Noise = 10.8dB Signal/Noise = 18.5dB

22 On certain traps of evolutionary engineering Problems related to the formulation of requirements: facilitate translation of target specifications into the language of evolution, including representations, fitness function and parameters of the algorithm. Autonomous systems: provide complete up-front specifications; Two identified traps: –Transient solutions; –Operational Range in the frequency domain; –Time constants

23 Transient Solutions Circuit under evaluation can take reasonable amount of time (~1sec) to achieve steady state, while evaluation time in the mili-seconds timescale  transient behavior evaluated; Behavior exhibited in the evaluation can be influenced by the previously circuit downloaded ; Due to parasitic as well as static capacitors in the chip ; GA usually eliminates transient solutions after some generations.

24 An example of transient behavior. The degradation shown from a) to d) occurred over the span of approximately 1 second. Illustration of Transient Behavior a) b) c) d)

25 Operational Range in the Frequency Domain Response of the half-wave rectifier for a frequency sweep from 500Hz to 5kHz (left). Deteriorated response at 50kHz. Circuit evolved at 2kHz does not work at more than 10kHz Circuit behavior should be evaluated for the overall frequency domain in which it is expected to work

26 Effect of Timescales Evolutionary design requires explicit formulation of assumptions often implicit to human designers; Example: evolved logic gates often present different behavior over a "frequency range" i.e. function with slow/DC signals as well as to faster input changing signals. –Evolution in small timescales: transient solutions; –Evolution in large timescales: slow gates; Solutions: –Mixtrinsic evolution: using combined simulation models; –Increase transient analysis duration to ‘catch’ operating point; –Decrease step of transient analysis: check circuit behavior after transition; –Increase output load to ensure a fast gate.

27 Time Constants Evolved NAND gate evaluated in the timescale of microsecond (until sec) microsecond second Evolved NAND gate using two different timescales Evolution in simulation