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Fully Parallel Learning Neural Network Chip for Real-time Control

Students: (Dr. Jin Liu), Borte Terlemez

Advisor: Dr. Martin Brooke

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Combustion Instability Control -Simulation Results Review

• Simulated Neural Net and Combustion

• One-frequency Results

• Multi-frequency Results

• Parameter Variation Results

• Added Noise Results

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Simulation SetupD

elay

1.5

ms

Del

ay li

ne

error error

UnstableCombustion Model

xu

Software Simulation of Neural Network Chip

uxxb

bxx

2

2 )(2

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One Frequency Plant without Control

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One Frequency Result

Time (second)

Eng

ine

Pre

ssur

eN

N W

eigh

t

1 4 2 3 4 2 1 3 2 3 4 3 5 1 5 4

f = 400Hzb =

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Two-Frequency Results

Time (Second)

NN

Wei

ght

Eng

ine

Pre

ssur

e

f = 400Hz 700Hzb =

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Parameter Variation Results

f = 400-600Hz = 0-0.008b = 1-100

Rate=1/sec Rate=50/sec

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10 % Added Noise Results

Uncontrolled Engine

Neural Network Controlled Engine

f=400Hz=0.005b=1

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Neural Network Chip Control of Combustion Instability

Del

ay 1

.5m

s

Del

ay li

ne

2.5

ms

8 ta

ps

error

400Hz

xx2/b -1)x+2x=u ...

xu

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Experimental Setup

Chip ControlSignals

5

Digital Output

1

Analog Input

Chip Output

Chip Input

Analog Output

8

National InstrumentAT-MIO-16E

National InstrumentAT-AO-10

Current to Voltage Conversion

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Test Box

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Experimental Result

f = 400Hz = 0.0b = 0.1

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More Resultsf = 400Hz = 0.0b = 0.1

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More Resultsf = 400Hz = 0.0b = 0.1

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Details of Initial Oscillation Suppression

Error Decreases

f = 400Hz = 0.0b = 0.1

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Details of the Continuously Adjusting Process

Error Increases

Error Decreases

f = 400Hz = 0.0b = 0.1

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Experiments with Run Time

f = 400Hz = 0.0b = 0.1

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Experiments with Damping Factor =0.001

f = 400Hz = 0.001b = 0.1

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Experiments with Damping Factor =0.002

f = 400Hz = 0.002b = 0.1

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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

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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

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New Chip

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Chip Architecture - Block Diagram

AB

A I

nput

s (3

0)

A Outputs (20)

B I

nput

s (3

0)

B Outputs (4) Biases, Clocks and Control Signals (18)

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Cell Schematics

CellCell

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Full Chip Spice Simulation after Parasitic Extraction

• Shift Register

• Weight Updating

• Current Outputs at Pads

• Clocking Scheme

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Shift Register

X=1msFirst 0 to 1at sh_in

X=1.48msFirst 0 to 1at sh_out_1r24 cycles of delay

X=15.4msFirst 0 to 1at sh_out_end720 cycles of delay

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Weight Updating

Shifted involtage

Weights

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Output Currents at Pads

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Clocking Scheme for Learning

Sh_in data

1

2

_learn

_randomfor three sub-nets

One clocking cycle is 20 s

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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

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Future Works

• Acoustic Oscillation Suppression

• Test of the New Chip

• Real Combustion System Control• Third Generation Chip (~10,000 Weights)

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Acoustic Oscillation Setup

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The Two Layer Board