1. 7/6/99 MITE1 Fully Parallel Learning Neural Network Chip for Real-time Control Students: (Dr. Jin Liu), Borte Terlemez Advisor: Dr. Martin Brooke

2. 7/6/99 MITE2 Combustion Instability Control - Simulation Results Review Simulated Neural Net and Combustion One-frequency Results Multi-frequency Results Parameter Variation Results Added Noise Results

3. 7/6/99 MITE3 Simulation Setup Delay 1.5 ms Delay line error Unstable Combustion Model xu Software Simulation of Neural Network Chip

4. 7/6/99 MITE4 One Frequency Plant without Control

5. 7/6/99 MITE5 One Frequency Result f = 400Hz b = 

6. 7/6/99 MITE6 Two-Frequency Results f = 400Hz 700Hz b = 

7. 7/6/99 MITE7 Parameter Variation Results f = 400-600Hz  = 0-0.008 b = 1-100 Rate=1/secRate=50/sec

8. 7/6/99 MITE8 10 % Added Noise Results Uncontrolled Engine Neural Network Controlled Engine f=400Hz  =0.005 b=1

9. 7/6/99 MITE9 Neural Network Chip Control of Combustion Instability Delay 1.5ms Delay line 2.5 ms 8 taps error  400Hz  x  x 2 /b -1)x+  2 x=u... xu

10. 7/6/99 MITE10 Experimental Setup

11. 7/6/99 MITE11 Test Box

12. 7/6/99 MITE12 Experimental Result f = 400Hz  = 0.0 b = 0.1

13. 7/6/99 MITE13 More Results f = 400Hz  = 0.0 b = 0.1

14. 7/6/99 MITE14 More Results f = 400Hz  = 0.0 b = 0.1

15. 7/6/99 MITE15 Details of Initial Oscillation Suppression Error Decreases f = 400Hz  = 0.0 b = 0.1

16. 7/6/99 MITE16 Details of the Continuously Adjusting Process Error Increases Error Decreases f = 400Hz  = 0.0 b = 0.1

17. 7/6/99 MITE17 Experiments with Run Time f = 400Hz  = 0.0 b = 0.1

18. 7/6/99 MITE18 Experiments with Damping Factor  =0.001 f = 400Hz  = 0.001 b = 0.1

19. 7/6/99 MITE19 Experiments with Damping Factor  =0.002 f = 400Hz  = 0.002 b = 0.1

20. 7/6/99 MITE20 Summary of NN Chip Control of Simulated Combustion Instability The NN chip can successfully suppress the combustion instabilities within around 1 sec. The NN chip continuously adjusts on-line to limit the engine output to be within a small magnitude. –I/O card delay and engine simulation delay 30 times longer than real time Weight leakage –Fixed learning step size

21. 7/6/99 MITE21 Improved Neural Network Chip in 0.35-  m Process Seven Time More Neuron Cells Two layers Each layer has 30 inputs instead of 10 Totally 720 neurons instead of 100 Adaptive Learning Step Size Capacitor charge sharing scheme Current charging and discharging scheme Partitioned Error Feedback Synchronized Learning, without stopping the clocks

22. 7/6/99 MITE22 New Chip

23. 7/6/99 MITE23 Chip Architecture - Block Diagram

24. 7/6/99 MITE24 Cell Schematics Cell

25. 7/6/99 MITE25 Full Chip Spice Simulation after Parasitic Extraction Shift Register Weight Updating Current Outputs at Pads Clocking Scheme

26. 7/6/99 MITE26 Shift Register X=1ms First 0 to 1 at sh_in X=1.48ms First 0 to 1 at sh_out_1r 24 cycles of delay X=15.4ms First 0 to 1 at sh_out_end 720 cycles of delay

27. 7/6/99 MITE27 Weight Updating Shifted in voltage Weights

28. 7/6/99 MITE28 Output Currents at Pads

29. 7/6/99 MITE29 Clocking Scheme for Learning Sh_in data 11 22  _learn  _random for three sub-nets One clocking cycle is 20  s

30. 7/6/99 MITE30 Conclusion Extensive software simulations to provide a solution for real-time control using the RWC algorithm, with direct feedback scheme Successful application of the analog neural network chip to control simulated dynamic, nonlinear system Improved chip resulted from the extensive hardware experiments Automated test method and system

31. 7/6/99 MITE31 Future Works Acoustic Oscillation Suppression Test of the New Chip Real Combustion System Control Third Generation Chip (~10,000 Weights )

32. 7/6/99 MITE32 Acoustic Oscillation Setup

33. 7/6/99 MITE33 The Two Layer Board