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

24,593 bytes added, 11:35, 6 April 2021
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==Introduction==
 
 
==Test Bed==
 
==Dataset==
 
[[File:FICS-PCB samples.png|center|thumb|500x500px|'''caption''']]
 
 
[[File:Samples per class in Microscope and DSLR subsets.png|center|thumb|500x500px|caption]]
 
 
[[File:Dataset processing and augmentation.png|center|thumb|500x500px|caption]]
 
 
[[File:Image augmentation for training samples.png|center|thumb|500x500px|caption]]
 
 
==Models==
 
===ResNet50===
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Resnet50 train and validation accuracy.png|thumb|500x500px|Train and validation accuracy trend over 1000 training epochs for ResNet50 model]]
|
|[[File:Resnet50 train and validation loss.png|thumb|500x500px|Train and validation loss trend over 1000 training epochs for ResNet50 model]]
|
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Resnet50 host confusion matrix.png|center|thumb|500x500px|Confusion matrix of ResNet50 model on host machine before quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Host machine, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.95740
| 0.89900
| 0.92728
| 1000
|-
| style="text-align:left;" | capacitor
| 0.97278
| 0.96500
| 0.96888
| 1000
|-
| style="text-align:left;" | diode
| 0.88558
| 0.95200
| 0.91759
| 1000
|-
| style="text-align:left;" | inductor
| 0.97006
| 0.97200
| 0.97103
| 1000
|-
| style="text-align:left;" | resistor
| 0.98882
| 0.97300
| 0.98085
| 1000
|-
| style="text-align:left;" | transistor
| 0.92262
| 0.93000
| 0.92629
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.94954
! 0.94850
! 0.94865
! 6000
|}
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Resnet50 target confusion matrix.png|center|thumb|500x500px|Confusion matrix of ResNet50 model on target device after quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Target device, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.96384
| 0.85300
| 0.90504
| 1000
|-
| style="text-align:left;" | capacitor
| 0.99068
| 0.95700
| 0.97355
| 1000
|-
| style="text-align:left;" | diode
| 0.83779
| 0.94000
| 0.88596
| 1000
|-
| style="text-align:left;" | inductor
| 0.94839
| 0.97400
| 0.96103
| 1000
|-
| style="text-align:left;" | resistor
| 0.97211
| 0.97600
| 0.97405
| 1000
|-
| style="text-align:left;" | transistor
| 0.89960
| 0.89600
| 0.89780
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.93540
! 0.93267
! 0.93290
! 6000
|}
|}
 
 
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<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Resnet50 cores utilization.png|thumb|500x500px|Utilization of CPU and DPU cores of ResNet50 model for 1, 2, and 4 threads]]
|
|[[File:Resnet50 DPU latency.png|thumb|500x500px|DPU latency of ResNet50 model for 1, 2, and 4 threads]]
|
|}
 
 
===ResNet101===
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Resnet101 train and validation accuracy.png|thumb|500x500px|Train and validation accuracy trend over 1000 training epochs for ResNet101 model]]
|
|[[File:Resnet101 train and validation loss.png|thumb|500x500px|Train and validation loss trend over 1000 training epochs for ResNet101 model]]
|
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Resnet101 host confusion matrix.png|center|thumb|500x500px|Confusion matrix of ResNet101 model on host machine before quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Host machine, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.96375
| 0.95700
| 0.96036
| 1000
|-
| style="text-align:left;" | capacitor
| 0.96373
| 0.98300
| 0.97327
| 1000
|-
| style="text-align:left;" | diode
| 0.96425
| 0.94400
| 0.95402
| 1000
|-
| style="text-align:left;" | inductor
| 0.98500
| 0.98500
| 0.98500
| 1000
|-
| style="text-align:left;" | resistor
| 0.98504
| 0.98800
| 0.98652
| 1000
|-
| style="text-align:left;" | transistor
| 0.96517
| 0.97000
| 0.96758
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.97116
! 0.97117
! 0.97112
! 6000
|}
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Resnet101 target confusion matrix.png|center|thumb|500x500px|Confusion matrix of ResNet101 model on target device after quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Target device, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.96288
| 0.88200
| 0.92067
| 1000
|-
| style="text-align:left;" | capacitor
| 0.95898
| 0.98200
| 0.97036
| 1000
|-
| style="text-align:left;" | diode
| 0.93965
| 0.90300
| 0.92096
| 1000
|-
| style="text-align:left;" | inductor
| 0.93719
| 0.95500
| 0.94601
| 1000
|-
| style="text-align:left;" | resistor
| 0.90428
| 0.99200
| 0.94611
| 1000
|-
| style="text-align:left;" | transistor
| 0.93896
| 0.92300
| 0.93091
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.94033
! 0.93950
! 0.93917
! 6000
|}
|}
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Resnet101 cores utilization.png|thumb|500x500px|Utilization of CPU and DPU cores of ResNet101 model for 1, 2, and 4 threads]]
|
|[[File:Resnet101 DPU latency.png|thumb|500x500px|DPU latency of ResNet101 model for 1, 2, and 4 threads]]
|
|}
 
 
===ResNet152===
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Resnet152 train and validation accuracy.png|thumb|500x500px|Train and validation accuracy trend over 1000 training epochs for ResNet152 model]]
|
|[[File:Resnet152 train and validation loss.png|thumb|500x500px|Train and validation loss trend over 1000 training epochs for ResNet152 model]]
|
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Resnet152 host confusion matrix.png|center|thumb|500x500px|Confusion matrix of ResNet152 model on host machine before quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Host machine, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.94553
| 0.97200
| 0.95858
| 1000
|-
| style="text-align:left;" | capacitor
| 0.95538
| 0.98500
| 0.96997
| 1000
|-
| style="text-align:left;" | diode
| 0.98298
| 0.92400
| 0.95258
| 1000
|-
| style="text-align:left;" | inductor
| 0.98584
| 0.97500
| 0.98039
| 1000
|-
| style="text-align:left;" | resistor
| 0.99390
| 0.97800
| 0.98589
| 1000
|-
| style="text-align:left;" | transistor
| 0.92899
| 0.95500
| 0.94181
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.96544
! 0.96483
! 0.96487
! 6000
|}
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Resnet152 target confusion matrix.png|center|thumb|500x500px|Confusion matrix of ResNet152 model on target device after quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Target device, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.91182
| 0.91000
| 0.91091
| 1000
|-
| style="text-align:left;" | capacitor
| 0.94460
| 0.98900
| 0.96629
| 1000
|-
| style="text-align:left;" | diode
| 0.96464
| 0.87300
| 0.91654
| 1000
|-
| style="text-align:left;" | inductor
| 0.94124
| 0.94500
| 0.94311
| 1000
|-
| style="text-align:left;" | resistor
| 0.94038
| 0.97800
| 0.95882
| 1000
|-
| style="text-align:left;" | transistor
| 0.90358
| 0.90900
| 0.90628
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.93438
! 0.93400
! 0.93366
! 6000
|}
|}
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Resnet152 cores utilization.png|thumb|500x500px|Utilization of CPU and DPU cores of ResNet152 model for 1, 2, and 4 threads]]
|
|[[File:Resnet152 DPU latency.png|thumb|500x500px|DPU latency of ResNet152 model for 1, 2, and 4 threads]]
|
|}
 
 
===InceptionV4===
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:InceptionV4 train and validation accuracy.png|thumb|500x500px|Train and validation accuracy trend over 1000 training epochs for InceptionV4 model]]
|
|[[File:InceptionV4 train and validation loss.png|thumb|500x500px|Train and validation loss trend over 1000 training epochs for InceptionV4 model]]
|
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:InceptionV4 host confusion matrix.png|center|thumb|500x500px|Confusion matrix of InceptionV4 model on host machine before quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Host machine, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.94524
| 0.86300
| 0.90225
| 1000
|-
| style="text-align:left;" | capacitor
| 0.98051
| 0.95600
| 0.96810
| 1000
|-
| style="text-align:left;" | diode
| 0.88384
| 0.87500
| 0.87940
| 1000
|-
| style="text-align:left;" | inductor
| 0.95575
| 0.97200
| 0.96381
| 1000
|-
| style="text-align:left;" | resistor
| 0.96847
| 0.98300
| 0.97568
| 1000
|-
| style="text-align:left;" | transistor
| 0.83670
| 0.91200
| 0.87273
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.92842
! 0.92683
! 0.92699
! 6000
|}
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:InceptionV4 target confusion matrix.png|center|thumb|500x500px|Confusion matrix of InceptionV4 model on target device after quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Target device, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.78158
| 0.89100
| 0.83271
| 1000
|-
| style="text-align:left;" | capacitor
| 0.99220
| 0.89000
| 0.93832
| 1000
|-
| style="text-align:left;" | diode
| 0.88553
| 0.82000
| 0.85151
| 1000
|-
| style="text-align:left;" | inductor
| 0.88973
| 0.94400
| 0.91606
| 1000
|-
| style="text-align:left;" | resistor
| 0.97319
| 0.98000
| 0.97658
| 1000
|-
| style="text-align:left;" | transistor
| 0.83282
| 0.80700
| 0.81971
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.89251
! 0.88867
! 0.88915
! 6000
|}
|}
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Inception v4 cores utilization.png|thumb|500x500px|Utilization of CPU and DPU cores of InceptionV4 model for 1, 2, and 4 threads]]
|
|[[File:Inception v4 DPU latency.png|thumb|500x500px|DPU latency of InceptionV4 model for 1, 2, and 4 threads]]
|
|}
 
 
===Inception ResNet V1===
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Inception ResNet V1 train and validation accuracy.png|thumb|500x500px|Train and validation accuracy trend over 1000 training epochs for Inception ResNet V1 model]]
|
|[[File:Inception ResNet V1 train and validation loss.png|thumb|500x500px|Train and validation loss trend over 1000 training epochs for Inception ResNet V1 model]]
|
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Inception ResNet V1 host confusion matrix.png|center|thumb|500x500px|Confusion matrix of Inception ResNet V1 model on host machine before quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Host machine, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.98274
| 0.96800
| 0.97531
| 1000
|-
| style="text-align:left;" | capacitor
| 0.97571
| 0.96400
| 0.96982
| 1000
|-
| style="text-align:left;" | diode
| 0.94889
| 0.98400
| 0.96613
| 1000
|-
| style="text-align:left;" | inductor
| 0.98085
| 0.97300
| 0.97691
| 1000
|-
| style="text-align:left;" | resistor
| 0.98211
| 0.98800
| 0.98504
| 1000
|-
| style="text-align:left;" | transistor
| 0.97278
| 0.96500
| 0.96888
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.97385
! 0.97367
! 0.97368
! 6000
|}
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Inception ResNet V1 target confusion matrix.png|center|thumb|500x500px|Confusion matrix of Inception ResNet V1 model on target device after quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Target device, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.84127
| 0.95400
| 0.89410
| 1000
|-
| style="text-align:left;" | capacitor
| 0.99787
| 0.93600
| 0.96594
| 1000
|-
| style="text-align:left;" | diode
| 0.94346
| 0.90100
| 0.92174
| 1000
|-
| style="text-align:left;" | inductor
| 0.95275
| 0.98800
| 0.97005
| 1000
|-
| style="text-align:left;" | resistor
| 0.94852
| 0.99500
| 0.97121
| 1000
|-
| style="text-align:left;" | transistor
| 0.93348
| 0.82800
| 0.87758
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.93622
! 0.93367
! 0.93344
! 6000
|}
|}
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Inception resnet v1 cores utilization.png|thumb|500x500px|Utilization of CPU and DPU cores of Inception ResNet V1 model for 1, 2, and 4 threads]]
|
|[[File:Inception resnet v1 DPU latency.png|thumb|500x500px|DPU latency of Inception ResNet V1 model for 1, 2, and 4 threads]]
|
|}
 
 
===Inception ResNet V2===
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Inception ResNet V2 train and validation accuracy.png|thumb|500x500px|Train and validation accuracy trend over 1000 training epochs for Inception ResNet V2 model]]
|
|[[File:Inception ResNet V2 train and validation loss.png|thumb|500x500px|Train and validation loss trend over 1000 training epochs for Inception ResNet V2 model]]
|
|}
 
 
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Inception ResNet V2 host confusion matrix.png|center|thumb|500x500px|Confusion matrix of Inception ResNet V2 model on host machine before quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Host machine, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.97872
| 0.96600
| 0.97232
| 1000
|-
| style="text-align:left;" | capacitor
| 0.99177
| 0.96400
| 0.97769
| 1000
|-
| style="text-align:left;" | diode
| 0.98963
| 0.95400
| 0.97149
| 1000
|-
| style="text-align:left;" | inductor
| 0.97931
| 0.99400
| 0.98660
| 1000
|-
| style="text-align:left;" | resistor
| 0.98213
| 0.98900
| 0.98555
| 1000
|-
| style="text-align:left;" | transistor
| 0.93365
| 0.98500
| 0.95864
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.97587
! 0.97533
! 0.97538
! 6000
|}
|}
 
 
lorem ipsum
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{| align="center" style = "background: transparent; margin: auto; width: 60%;"
|-
| width=200px style = " vertical-align: center; " |[[File:Inception ResNet V2 target confusion matrix.png|center|thumb|500x500px|Confusion matrix of Inception ResNet V2 model on target device after quantization]]
| width=200px style = " vertical-align: center; " |
{| class="wikitable" style="margin: auto; text-align: center;"
|+ Target device, classification report
|- style="font-weight:bold;"
! Class
! Precision
! Recall
! F1-score
! Support
|-
| style="text-align:left;" | IC
| 0.91735
| 0.89900
| 0.90808
| 1000
|-
| style="text-align:left;" | capacitor
| 0.99466
| 0.93200
| 0.96231
| 1000
|-
| style="text-align:left;" | diode
| 0.98793
| 0.90000
| 0.94192
| 1000
|-
| style="text-align:left;" | inductor
| 0.92066
| 0.99800
| 0.95777
| 1000
|-
| style="text-align:left;" | resistor
| 0.96970
| 0.99200
| 0.98072
| 1000
|-
| style="text-align:left;" | transistor
| 0.87887
| 0.93600
| 0.90654
| 1000
|- style="font-weight:bold;"
! Weighted avg
! 0.94486
! 0.94283
! 0.94289
! 6000
|}
|}
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:Inception resnet v2 cores utilization.png|thumb|500x500px|Utilization of CPU and DPU cores of Inception ResNet V2 model for 1, 2, and 4 threads]]
|
|[[File:Inception resnet v2 DPU latency.png|thumb|500x500px|DPU latency of Inception ResNet V2 model for 1, 2, and 4 threads]]
|
|}
 
 
==Comparison==
 
[[File:Pre and post quantization accuracy.png|center|thumb|500x500px|Models pre and post quantization accuracy with vai_q_tensorflow tool]]
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
|
|[[File:DPU Kernel parameters size.png|thumb|500x500px|Deployed models DPU Kernel parameters size]]
|
|[[File:DPU Kernel total tensor count.png|thumb|500x500px|Deployed models DPU Kernel total tensor count]]
|
|}
 
 
<!--Start of table definition-->
{|style="background:transparent; color:black" border="0" height="550" align="center" valign="bottom" cellpadding=10px cellspacing=0px
|-align="center"
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|[[File:DPU-00 core latency for 1-2-4 threads.png|thumb|500x500px|Deployed models DPU-00 core latency for [1,2,4] threads]]
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|[[File:DPU-01 core latency for 1-2-4 threads.png|thumb|500x500px|Deployed models DPU-01 core latency for [1,2,4] threads]]
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|}
 
 
[[File:DPU throughput for 1-2-4 threads.png|center|thumb|500x500px|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, [https://eprint.iacr.org/2020/366.pdf ''FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection''], July 2020.
*Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, [https://arxiv.org/pdf/1512.03385.pdf ''Deep Residual Learning for Image Recognition''], December 2015.
*Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi, [https://arxiv.org/pdf/1602.07261.pdf,''Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning''], August 2016.
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