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Fully Parallel Learning Neural Network Chip for Real-time Control

This research presents a novel fully parallel learning neural network chip designed for real-time control of combustion instability. Conducted by Dr. Jin Liu under the guidance of Dr. Martin Brooke, the study includes extensive simulations of neural networks and combustion dynamics. Key findings demonstrate the chip's capability to suppress instabilities effectively, with continuous adjustments to maintain engine output within desired limits. The upgraded chip architecture features enhanced neuron capacity and adaptive learning mechanisms, improving performance in dynamic systems and showcasing promising results for future real-world applications.

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Fully Parallel Learning Neural Network Chip for Real-time Control

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

  2. Combustion Instability Control -Simulation Results Review • Simulated Neural Net and Combustion • One-frequency Results • Multi-frequency Results • Parameter Variation Results • Added Noise Results

  3. u Unstable x Combustion Model Delay 1.5 ms error error Delay line Software Simulation of Neural Network Chip Simulation Setup

  4. One Frequency Plant without Control

  5. One Frequency Result f = 400Hz b = 

  6. Two-Frequency Results f = 400Hz 700Hz b = 

  7. Rate=1/sec Rate=50/sec Parameter Variation Results f = 400-600Hz z = 0-0.008 b = 1-100

  8. Uncontrolled Engine Neural Network Controlled Engine 10 % Added Noise Results f=400Hz z=0.005 b=1

  9. .. . u x x+2zw(x2/b -1)x+w2x=u w = 2*p*(400Hz) Delay 1.5ms error 2.5 ms 8 taps Delay line Neural Network Chip Control of Combustion Instability

  10. Experimental Setup

  11. Test Box

  12. Experimental Result f = 400Hz z = 0.0 b = 0.1

  13. f = 400Hz z = 0.0 b = 0.1 More Results

  14. f = 400Hz z = 0.0 b = 0.1 More Results

  15. Details of Initial Oscillation Suppression Error Decreases f = 400Hz z = 0.0 b = 0.1

  16. Details of the Continuously Adjusting Process Error Decreases f = 400Hz z = 0.0 b = 0.1 Error Increases

  17. Experiments with Run Time f = 400Hz z = 0.0 b = 0.1

  18. Experiments with Damping Factor z=0.001 f = 400Hz z = 0.001 b = 0.1

  19. Experiments with Damping Factor z=0.002 f = 400Hz z = 0.002 b = 0.1

  20. 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. Improved Neural Network Chip in 0.35- mm 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. New Chip

  23. Chip Architecture - Block Diagram

  24. Cell Schematics Cell Cell

  25. Full Chip Spice Simulation after Parasitic Extraction • Shift Register • Weight Updating • Current Outputs at Pads • Clocking Scheme

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

  27. Weight Updating Shifted in voltage Weights

  28. Output Currents at Pads

  29. Sh_in data 1 2 _learn _random for three sub-nets Clocking Scheme for Learning One clocking cycle is 20 ms

  30. 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. Future Works • Acoustic Oscillation Suppression • Test of the New Chip • Real Combustion System Control • Third Generation Chip (~10,000 Weights)

  32. Acoustic Oscillation Setup

  33. The Two Layer Board

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