ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 2

Info Box Applies to Machine Learning Applies to Machine Learning Technical Notes

History

Version	Date	Notes
1.0.0	March 2021	First public release

Dataset

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Models

ResNet50

Train and validation accuracy trend over 1000 training epochs for ResNet50 model

Train and validation loss trend over 1000 training epochs for ResNet50 model

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Confusion matrix of ResNet50 model on host machine before quantization

Host machine, classification report Class Precision Recall F1-score Support IC 0.95740 0.89900 0.92728 1000 capacitor 0.97278 0.96500 0.96888 1000 diode 0.88558 0.95200 0.91759 1000 inductor 0.97006 0.97200 0.97103 1000 resistor 0.98882 0.97300 0.98085 1000 transistor 0.92262 0.93000 0.92629 1000 Weighted avg 0.94954 0.94850 0.94865 6000

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Confusion matrix of ResNet50 model on target device after quantization

Target device, classification report Class Precision Recall F1-score Support IC 0.96384 0.85300 0.90504 1000 capacitor 0.99068 0.95700 0.97355 1000 diode 0.83779 0.94000 0.88596 1000 inductor 0.94839 0.97400 0.96103 1000 resistor 0.97211 0.97600 0.97405 1000 transistor 0.89960 0.89600 0.89780 1000 Weighted avg 0.93540 0.93267 0.93290 6000

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Utilization of CPU and DPU cores of ResNet50 model for 1, 2, and 4 threads

DPU latency of ResNet50 model for 1, 2, and 4 threads

ResNet101

Train and validation accuracy trend over 1000 training epochs for ResNet101 model

Train and validation loss trend over 1000 training epochs for ResNet101 model

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Confusion matrix of ResNet101 model on host machine before quantization

Host machine, classification report Class Precision Recall F1-score Support IC 0.96375 0.95700 0.96036 1000 capacitor 0.96373 0.98300 0.97327 1000 diode 0.96425 0.94400 0.95402 1000 inductor 0.98500 0.98500 0.98500 1000 resistor 0.98504 0.98800 0.98652 1000 transistor 0.96517 0.97000 0.96758 1000 Weighted avg 0.97116 0.97117 0.97112 6000

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Confusion matrix of ResNet101 model on target device after quantization

Target device, classification report Class Precision Recall F1-score Support IC 0.96288 0.88200 0.92067 1000 capacitor 0.95898 0.98200 0.97036 1000 diode 0.93965 0.90300 0.92096 1000 inductor 0.93719 0.95500 0.94601 1000 resistor 0.90428 0.99200 0.94611 1000 transistor 0.93896 0.92300 0.93091 1000 Weighted avg 0.94033 0.93950 0.93917 6000

Utilization of CPU and DPU cores of ResNet101 model for 1, 2, and 4 threads

DPU latency of ResNet101 model for 1, 2, and 4 threads

ResNet152

Train and validation accuracy trend over 1000 training epochs for ResNet152 model

Train and validation loss trend over 1000 training epochs for ResNet152 model

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Confusion matrix of ResNet152 model on host machine before quantization

Host machine, classification report Class Precision Recall F1-score Support IC 0.94553 0.97200 0.95858 1000 capacitor 0.95538 0.98500 0.96997 1000 diode 0.98298 0.92400 0.95258 1000 inductor 0.98584 0.97500 0.98039 1000 resistor 0.99390 0.97800 0.98589 1000 transistor 0.92899 0.95500 0.94181 1000 Weighted avg 0.96544 0.96483 0.96487 6000

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Confusion matrix of ResNet152 model on target device after quantization

Target device, classification report Class Precision Recall F1-score Support IC 0.91182 0.91000 0.91091 1000 capacitor 0.94460 0.98900 0.96629 1000 diode 0.96464 0.87300 0.91654 1000 inductor 0.94124 0.94500 0.94311 1000 resistor 0.94038 0.97800 0.95882 1000 transistor 0.90358 0.90900 0.90628 1000 Weighted avg 0.93438 0.93400 0.93366 6000

Utilization of CPU and DPU cores of ResNet152 model for 1, 2, and 4 threads

DPU latency of ResNet152 model for 1, 2, and 4 threads

InceptionV4

Train and validation accuracy trend over 1000 training epochs for InceptionV4 model

Train and validation loss trend over 1000 training epochs for InceptionV4 model

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Confusion matrix of InceptionV4 model on host machine before quantization

Host machine, classification report Class Precision Recall F1-score Support IC 0.94524 0.86300 0.90225 1000 capacitor 0.98051 0.95600 0.96810 1000 diode 0.88384 0.87500 0.87940 1000 inductor 0.95575 0.97200 0.96381 1000 resistor 0.96847 0.98300 0.97568 1000 transistor 0.83670 0.91200 0.87273 1000 Weighted avg 0.92842 0.92683 0.92699 6000

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Confusion matrix of InceptionV4 model on target device after quantization

Target device, classification report Class Precision Recall F1-score Support IC 0.78158 0.89100 0.83271 1000 capacitor 0.99220 0.89000 0.93832 1000 diode 0.88553 0.82000 0.85151 1000 inductor 0.88973 0.94400 0.91606 1000 resistor 0.97319 0.98000 0.97658 1000 transistor 0.83282 0.80700 0.81971 1000 Weighted avg 0.89251 0.88867 0.88915 6000

Utilization of CPU and DPU cores of InceptionV4 model for 1, 2, and 4 threads

DPU latency of InceptionV4 model for 1, 2, and 4 threads

Inception ResNet V1

Train and validation accuracy trend over 1000 training epochs for Inception ResNet V1 model

Train and validation loss trend over 1000 training epochs for Inception ResNet V1 model

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Confusion matrix of Inception ResNet V1 model on host machine before quantization

Host machine, classification report Class Precision Recall F1-score Support IC 0.98274 0.96800 0.97531 1000 capacitor 0.97571 0.96400 0.96982 1000 diode 0.94889 0.98400 0.96613 1000 inductor 0.98085 0.97300 0.97691 1000 resistor 0.98211 0.98800 0.98504 1000 transistor 0.97278 0.96500 0.96888 1000 Weighted avg 0.97385 0.97367 0.97368 6000

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Confusion matrix of Inception ResNet V1 model on target device after quantization

Target device, classification report Class Precision Recall F1-score Support IC 0.84127 0.95400 0.89410 1000 capacitor 0.99787 0.93600 0.96594 1000 diode 0.94346 0.90100 0.92174 1000 inductor 0.95275 0.98800 0.97005 1000 resistor 0.94852 0.99500 0.97121 1000 transistor 0.93348 0.82800 0.87758 1000 Weighted avg 0.93622 0.93367 0.93344 6000

Utilization of CPU and DPU cores of Inception ResNet V1 model for 1, 2, and 4 threads

DPU latency of Inception ResNet V1 model for 1, 2, and 4 threads

Inception ResNet V2

Train and validation accuracy trend over 1000 training epochs for Inception ResNet V2 model

Train and validation loss trend over 1000 training epochs for Inception ResNet V2 model

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Confusion matrix of Inception ResNet V2 model on host machine before quantization

Host machine, classification report Class Precision Recall F1-score Support IC 0.97872 0.96600 0.97232 1000 capacitor 0.99177 0.96400 0.97769 1000 diode 0.98963 0.95400 0.97149 1000 inductor 0.97931 0.99400 0.98660 1000 resistor 0.98213 0.98900 0.98555 1000 transistor 0.93365 0.98500 0.95864 1000 Weighted avg 0.97587 0.97533 0.97538 6000

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Confusion matrix of Inception ResNet V2 model on target device after quantization

Target device, classification report Class Precision Recall F1-score Support IC 0.91735 0.89900 0.90808 1000 capacitor 0.99466 0.93200 0.96231 1000 diode 0.98793 0.90000 0.94192 1000 inductor 0.92066 0.99800 0.95777 1000 resistor 0.96970 0.99200 0.98072 1000 transistor 0.87887 0.93600 0.90654 1000 Weighted avg 0.94486 0.94283 0.94289 6000

Utilization of CPU and DPU cores of Inception ResNet V2 model for 1, 2, and 4 threads

DPU latency of Inception ResNet V2 model for 1, 2, and 4 threads

Comparison

Models pre and post quantization accuracy with vai_q_tensorflow tool

Deployed models DPU Kernel parameters size

Deployed models DPU Kernel total tensor count

Deployed models DPU-00 core latency for [1,2,4] threads

Deployed models DPU-01 core latency for [1,2,4] threads

Deployed models DPU throughput for 1, 2, and 4 threads

Useful links

• Hangwei Lu, Dhwani Mehta, Olivia Paradis, Navid Asadizanjani, Mark Tehranipoor, Damon L. Woodard, FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection, July 2020.
• Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, December 2015.
• Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, August 2016.