1 5. Application Examples 5.1. Programmable compensation for analog circuits (Optimal tuning) 5.2. Programmable delays in high-speed digital circuits (Clock skew compensation) 5.3. Automated discovery – Invention by Genetic Programming (Creative Design) 5.4. EDA Tools, analog circuit design 5.5. Adaptation to extreme temperature electronics (Survivability by EHW) 5.6. Fault-tolerance and fault-recovery 5.7. Evolvable antennas (In-field adaptation to changing environment) 5.8. Adaptive filters (Function change as result of mission change) 5.9 Evolution of controllers

2 Evolution of Filters Binary representation used both in simulation and HW experiments: –Simulation experiments using SPICE models of the first FPTA chip; –Hardware experiments using FPTA-2 chip; Circuits evaluated in the frequency domain: –Simulation: small signal analysis in SPICE; –Real hardware: FFT of the circuit transient response.

3 Filter Evolution in Simulation Cell Topology: Small capacitors (0.1nF) placed between drain and gate to explore Miller effect Each cell has 8 fixed MOS transistors, 24 switches, 4 capacitors

4 Filter Evolution in simulation where: N is the number of samples in the frequency domain; O i is the circuit output in the frequency domain; T i is the target output in the frequency domain (low-pass, band-pass, etc); w i are weights whose values should be carefully set. Weight values are usually larger in the passing band comparing to the stop band. Fitness Evaluation Function →  w i |O i – T i | i=0 N

5 Filter Evolution in Simulation An example of a band-pass filter evolved on the 4 FPTA cells Roll-off of 35 dB/decade Roll-off Of 40 dB/decade Roll-off of -20 dB/decade InputOutput Wide Band Filter: Gain of 10 dB between 100kHz and 1MHz with roll-off of 40 dB/decade before 100kHz and -20 dB/decade after 1Mhz. Narrow Band Filter: Gain of 2 dB between 1kHz and 10kHz with roll-off of -35dB/decade. Stop band below 100Hz and above 100kHz.

6 Filter Evolution in Hardware Reconfigurable device: FPTA-2 Experiments performed on SABLES platform: about 5 minutes evolution time; Reconfigurable HW FPTA-2 sin(2  f 1 t) In(t) = + sin(2  f 2 t) Output FFT Computation in the DSP f 1 : Filter Cut-off frequency f 2 : Stop-band starting frequency Fitness function maximizes output FFT component at f 1 and minimizes the one at f 2.

7 Filter Evolution in Hardware Low-pass (f 1/2 = 1kHz/10kHz) and high-pass filters (f 2/1 = 1kHz/10kHz); Use of 10 cells of the FPTA-2 chip; Evolved Low-Pass Filter Evolved High-Pass Filter Input Output Input Output

8 Adaptive filters Genetic Algorithm self-tunes response of the filter (Simulation experiment); Adaptation based on filter inputs: Hardware experiment ; Goal: Function change as result of mission change.

9 Reconfigurable “Adaptive” Band-Pass 5kHz, 25kHz Evolved in Simulation Filter 1 Gain = 11.4dB Roll-off: 34dB/dec, -30dB/dec 1 2 Filter 2 Gain = 9dB Roll-off: 43dB/dec, -70dB/dec GA changes circuit topology through reconfiguration to respond to a new functional (frequency response) requirement

10 Evolvable Adaptive Filter in Hardware Demonstrate that FPTA2 can synthesize low and high pass filters without external capacitors: same piece of hardware can be reconfigured to realize low-pass and high-pass frequency responses Evolution of adaptive functionality: Filter reacts to change in the input signal –Evolution of filters that amplify strongest input signal and attenuate weakest signal (“noise”); –Circuit ‘does not know’ frequency spectrum at the input; –Adaptation through reconfiguration: new circuit evolved to cope with changes at the input signal.

11 Evolvable Adaptive Filter in Hardware Input signal: –Sum of two signals: 10kHz and 25kHz tones –Strong (signal) and Weak (noise) tones not known a priori FPTA cells: –Explore resistance of switches: partly opened/closed switches; –Four cells constrained to be inverters; –Evolved connections among cells Fitness Function: –Evaluate the FFT of output –Amplify strong signal, attenuate weak signal Genetic Algorithm: –400 individuals/200 generations; –Time on SABLES: 5 min

12 Adaptive Filter Results Filter characteristic: 10kHz : -2.86dB attenuation 20kHz : -15.8dB attenuation Filter characteristic: 10kHz : -12.5dB attenuation 20kHz : -4.8dB attenuation Input Signal/Noise = 4.1dB Output Signal/Noise = 16.9dB Input Signal/Noise = 10.8dB Output Signal/Noise = 18.5dB