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5. Application Examples

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

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5. Application Examples

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  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 1

  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. 2

  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 3

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

  5. Filter Evolution in Simulation Input Output Roll-off Of 40 dB/decade Roll-off of -20 dB/decade Roll-off of 35 dB/decade An example of a band-pass filter evolved on the 4 FPTA cells • 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. 5

  6. Filter Evolution in Hardware sin(2f1t) In(t) = + sin(2f2t) Reconfigurable HW FPTA-2 FFT Computation in the DSP Output f1: Filter Cut-off frequency f2: Stop-band starting frequency • Reconfigurable device: FPTA-2 • Experiments performed on SABLES platform: about 5 minutes evolution time; Fitness function maximizes output FFT component at f1 and minimizes the one at f2. 6

  7. Filter Evolution in Hardware Evolved Low-Pass Filter Evolved High-Pass Filter Input Input Output Output • Low-pass (f1/2 = 1kHz/10kHz) and high-pass filters (f2/1 = 1kHz/10kHz); • Use of 10 cells of the FPTA-2 chip; 7

  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. 8

  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 9

  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. 10

  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 11

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

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