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Copyright © 2019 by AI4quant, Inc. [TW]
AI model used in
smart manufacturer
Jason Chuang
2019-09-06
Copyright © 2019 by AI4quant, Inc. [TW]
Major Roles
in
AI team
Credit:
陳昇瑋
2
Copyright © 2019 by AI4quant, Inc. [TW]
AI: 4 elements
3
Big data ML Algorithms
Computation
Power
Domain
Knowledge
Copyright © 2019 by AI4quant, Inc. [TW]
AI data engineering 金字塔
4
Copyright © 2019 by AI4quant, Inc. [TW]
The hardest part of ML isn’t ML. It’s data.
“Hidden Technical Debt in Machine Learning Systems,” Google NIPS 2015
5
Copyright © 2019 by AI4quant, Inc. [TW]
經濟部工業局與NVIDIA的開發競賽
• https://www.nvidia.com/zh-tw/autonomous-machines/jetson-
challenge/
• 嵌入式新創開發競賽2019 HackIDB x NVIDIA
• From March 2019 to July 2019
• NVIDIA AGX Xavier
Copyright © 2019 by AI4quant, Inc. [TW]
Edge computing for AI inference
Image Classification
Object Detection
Image Segmentation(2018)
7
Copyright © 2019 by AI4quant, Inc. [TW]
Cloud Computing AI v.s. Edge Computing AI inference
● Cloud Computing inference
● Mostly 2012-2017
● Good computing power
● More storage space
● Network latency :
250~1250 ms
8
● Edge Computing inference
● Before 2016, the computation
latency: 3 sec.
● After 2016, the computation
latency drops to 50 ms
(around ~20 images/ sec)
Training mostly still in Cloud
Ref: https://udn.com/news/story/6871/3856742
Copyright © 2019 by AI4quant, Inc. [TW]
Industrial AI applications
Credit: Nvidia
9
Copyright © 2019 by AI4quant, Inc. [TW]
可識別的材質
• 金屬箔面/機殼
• 半導體 / 精密零件
• 紡織品
• 鏡面 / 玻璃
Copyright © 2019 by AI4quant, Inc. [TW]
Example:Screw
Copyright © 2019 by AI4quant, Inc. [TW]
Inspection : 2 Main Scenarios
● Credit: Nvidia
12
Copyright © 2019 by AI4quant, Inc. [TW]
3 Main parties
Credit: Nvidia
13
Copyright © 2019 by AI4quant, Inc. [TW]
AOI & Decision Flow
Credit: Nvidia
14
Copyright © 2019 by AI4quant, Inc. [TW]
AOI & Decision Flow
Credit: Nvidia
15
Copyright © 2019 by AI4quant, Inc. [TW]
AOI & Decision Flow
Credit:Nvidia
16
Copyright © 2019 by AI4quant, Inc. [TW]
PCBA manufacturing & AOI Inspection
Credit: Nvidia
17
Copyright © 2019 by AI4quant, Inc. [TW]
PCB capacitor
Credit: Nvidia
18
Copyright © 2019 by AI4quant, Inc. [TW]
Training to Deployment
Credit: Nvidia
19
Copyright © 2019 by AI4quant, Inc. [TW]
Use case: AOI speed comparison
● Traditionally by human eyes,
● Product line: 23
● 4 Inspectors, missing rate 5%
● AOI hourly throughput, 600K/daily
● Max limit: each product line,
20K/hour = 11,040 K/daily
● Inspection throughput: 300 K/ person,
day
● Inspection throughput: 1.2 M/ daily
20
● Deep Learning system,
● Computer hardware: mid-high range
desktop computer+ Nvidia GPU:
100K~150K NTD
● Software: Open-source + deep
learning model
● Quality: the missing rate controlled
under 0.01%, inspectors only need to
inspect 5% of total pictures
● Inspection throughput : 166.67/ sec
● Inspection throughput: 14.4 M/ daily
Copyright © 2019 by AI4quant, Inc. [TW]
Image Net competition
Copyright © 2019 by AI4quant, Inc. [TW]
Revolution of Depth vs Classification Accuracy
Copyright © 2019 by AI4quant, Inc. [TW]
CoreML Benchmark-Pick a DNN for your
mobile architecture
Ref:https://www.slideshare.net/anirudhkoul/squeezing-deep-learning-into-mobile-phones
Copyright © 2019 by AI4quant, Inc. [TW]
EfficientNet
Copyright © 2019 by AI4quant, Inc. [TW]
EfficientNet-B0
Copyright © 2019 by AI4quant, Inc. [TW]
技術上的考量
•資料前處理作法
•演算法和模型介紹(比賽策略的設計)
•開發環境:HiCloud平台與NVIDIA
tools(TensorRT)
Copyright © 2019 by AI4quant, Inc. [TW]
Strategy
• latency:
• Computation (# of parameters):
• 減少參數量: 輕量化網路架構 (depth wise separable convolution)
• 減少 width, image size
• TensorRT加速
• accuracy:
• 因為數據太少, 不容易正確計算準確率, 且容易overfit
• 使用pretrained weights (用CIFAR10在HiCloud上訓練)
• 訓練的照片image augmentation
Copyright © 2019 by AI4quant, Inc. [TW]
Data Preprocessing
• Preprocess:
• 只考慮電容,其他元件的瑕疵標示錯誤太多,沒有標準答案
• 50%的照片長邊都在64p以下,將照片保持比例縮放成64x64的方
塊
• 從所有顏色的大照片切出元件小圖,圖片量變~4倍
• Training image augmentation
• 極小尺度旋轉+變形+位移+上下左右翻轉, 180度旋轉
• 色彩, 亮度, 對比調整, 加噪音, 高斯模糊
• Upsample瑕疵照片: Defect : OK ~ 1:20->1:9
Copyright © 2019 by AI4quant, Inc. [TW]
Put to center
Copyright © 2019 by AI4quant, Inc. [TW]
Image augmentation (imgaug)
Copyright © 2019 by AI4quant, Inc. [TW]
Tensornets pre-trained weight
• https://github.com/taehoonlee/tensornets
Copyright © 2019 by AI4quant, Inc. [TW]
Training Loss v.s. Epoch
Copyright © 2019 by AI4quant, Inc. [TW]
Model accuracy
Copyright © 2019 by AI4quant, Inc. [TW]
Metrics- Accuracy
Copyright © 2019 by AI4quant, Inc. [TW]
MCC and Latency on validation set
Copyright © 2019 by AI4quant, Inc. [TW]
Learn from the competition report
•Latency is the key, Python multi-thread to
improve latency
•Smaller customized AI model can make the
speed even faster
Copyright © 2019 by AI4quant, Inc. [TW] 37
Copyright © 2019 by AI4quant, Inc. [TW]
A Brief Comparison of Edge Computing Devices
Ref: https://www.ideas2it.com/blogs/comparison-edge-computing-devices/
38
Features/De
vices
Google Coral(Dev
Board)
Intel Movidius
(Compute stick 2)
Intel’s UP Squared
AI Vision X
Developer Kit
NVIDIA Jetson
(Nano)
Raspberry Pi (3 b)
CPU Quad Arm Cortex-
A53,Cortex-M4F
QuadCore 1.6GHz
Atom x7-E3950
QuadCore 1.43GHz
ARM A57
QuadCore 1.2GHz
Broadcom BCM2837
GPU Integrated GC7000
Lite Graphics
Integrated Intel HD
Graphics 505
128-core Maxwell
GPU
N/A
Dedicated
HW for Deep
Learning,
ANN, CV
Google Edge TPU
coprocessor
16 Core Myriad X (Opt)16 Core Myriad
X
N/A N/A
Copyright © 2019 by AI4quant, Inc. [TW]
High performance & scalability for edge
● Nvidia -> TensorRT
● Intel -> OpenVINO
Copyright © 2019 by AI4quant, Inc. [TW]
Predictive Maintenance
Predictive Modeling
System
2017 US patent
2035-05-28 expire
40
Ref: https://patents.google.com/patent/US9699049
Copyright © 2019 by AI4quant, Inc. [TW]
Thank you!
● Q&A

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AI model in smart manufacturer

  • 1. Copyright © 2019 by AI4quant, Inc. [TW] AI model used in smart manufacturer Jason Chuang 2019-09-06
  • 2. Copyright © 2019 by AI4quant, Inc. [TW] Major Roles in AI team Credit: 陳昇瑋 2
  • 3. Copyright © 2019 by AI4quant, Inc. [TW] AI: 4 elements 3 Big data ML Algorithms Computation Power Domain Knowledge
  • 4. Copyright © 2019 by AI4quant, Inc. [TW] AI data engineering 金字塔 4
  • 5. Copyright © 2019 by AI4quant, Inc. [TW] The hardest part of ML isn’t ML. It’s data. “Hidden Technical Debt in Machine Learning Systems,” Google NIPS 2015 5
  • 6. Copyright © 2019 by AI4quant, Inc. [TW] 經濟部工業局與NVIDIA的開發競賽 • https://www.nvidia.com/zh-tw/autonomous-machines/jetson- challenge/ • 嵌入式新創開發競賽2019 HackIDB x NVIDIA • From March 2019 to July 2019 • NVIDIA AGX Xavier
  • 7. Copyright © 2019 by AI4quant, Inc. [TW] Edge computing for AI inference Image Classification Object Detection Image Segmentation(2018) 7
  • 8. Copyright © 2019 by AI4quant, Inc. [TW] Cloud Computing AI v.s. Edge Computing AI inference ● Cloud Computing inference ● Mostly 2012-2017 ● Good computing power ● More storage space ● Network latency : 250~1250 ms 8 ● Edge Computing inference ● Before 2016, the computation latency: 3 sec. ● After 2016, the computation latency drops to 50 ms (around ~20 images/ sec) Training mostly still in Cloud Ref: https://udn.com/news/story/6871/3856742
  • 9. Copyright © 2019 by AI4quant, Inc. [TW] Industrial AI applications Credit: Nvidia 9
  • 10. Copyright © 2019 by AI4quant, Inc. [TW] 可識別的材質 • 金屬箔面/機殼 • 半導體 / 精密零件 • 紡織品 • 鏡面 / 玻璃
  • 11. Copyright © 2019 by AI4quant, Inc. [TW] Example:Screw
  • 12. Copyright © 2019 by AI4quant, Inc. [TW] Inspection : 2 Main Scenarios ● Credit: Nvidia 12
  • 13. Copyright © 2019 by AI4quant, Inc. [TW] 3 Main parties Credit: Nvidia 13
  • 14. Copyright © 2019 by AI4quant, Inc. [TW] AOI & Decision Flow Credit: Nvidia 14
  • 15. Copyright © 2019 by AI4quant, Inc. [TW] AOI & Decision Flow Credit: Nvidia 15
  • 16. Copyright © 2019 by AI4quant, Inc. [TW] AOI & Decision Flow Credit:Nvidia 16
  • 17. Copyright © 2019 by AI4quant, Inc. [TW] PCBA manufacturing & AOI Inspection Credit: Nvidia 17
  • 18. Copyright © 2019 by AI4quant, Inc. [TW] PCB capacitor Credit: Nvidia 18
  • 19. Copyright © 2019 by AI4quant, Inc. [TW] Training to Deployment Credit: Nvidia 19
  • 20. Copyright © 2019 by AI4quant, Inc. [TW] Use case: AOI speed comparison ● Traditionally by human eyes, ● Product line: 23 ● 4 Inspectors, missing rate 5% ● AOI hourly throughput, 600K/daily ● Max limit: each product line, 20K/hour = 11,040 K/daily ● Inspection throughput: 300 K/ person, day ● Inspection throughput: 1.2 M/ daily 20 ● Deep Learning system, ● Computer hardware: mid-high range desktop computer+ Nvidia GPU: 100K~150K NTD ● Software: Open-source + deep learning model ● Quality: the missing rate controlled under 0.01%, inspectors only need to inspect 5% of total pictures ● Inspection throughput : 166.67/ sec ● Inspection throughput: 14.4 M/ daily
  • 21. Copyright © 2019 by AI4quant, Inc. [TW] Image Net competition
  • 22. Copyright © 2019 by AI4quant, Inc. [TW] Revolution of Depth vs Classification Accuracy
  • 23. Copyright © 2019 by AI4quant, Inc. [TW] CoreML Benchmark-Pick a DNN for your mobile architecture Ref:https://www.slideshare.net/anirudhkoul/squeezing-deep-learning-into-mobile-phones
  • 24. Copyright © 2019 by AI4quant, Inc. [TW] EfficientNet
  • 25. Copyright © 2019 by AI4quant, Inc. [TW] EfficientNet-B0
  • 26. Copyright © 2019 by AI4quant, Inc. [TW] 技術上的考量 •資料前處理作法 •演算法和模型介紹(比賽策略的設計) •開發環境:HiCloud平台與NVIDIA tools(TensorRT)
  • 27. Copyright © 2019 by AI4quant, Inc. [TW] Strategy • latency: • Computation (# of parameters): • 減少參數量: 輕量化網路架構 (depth wise separable convolution) • 減少 width, image size • TensorRT加速 • accuracy: • 因為數據太少, 不容易正確計算準確率, 且容易overfit • 使用pretrained weights (用CIFAR10在HiCloud上訓練) • 訓練的照片image augmentation
  • 28. Copyright © 2019 by AI4quant, Inc. [TW] Data Preprocessing • Preprocess: • 只考慮電容,其他元件的瑕疵標示錯誤太多,沒有標準答案 • 50%的照片長邊都在64p以下,將照片保持比例縮放成64x64的方 塊 • 從所有顏色的大照片切出元件小圖,圖片量變~4倍 • Training image augmentation • 極小尺度旋轉+變形+位移+上下左右翻轉, 180度旋轉 • 色彩, 亮度, 對比調整, 加噪音, 高斯模糊 • Upsample瑕疵照片: Defect : OK ~ 1:20->1:9
  • 29. Copyright © 2019 by AI4quant, Inc. [TW] Put to center
  • 30. Copyright © 2019 by AI4quant, Inc. [TW] Image augmentation (imgaug)
  • 31. Copyright © 2019 by AI4quant, Inc. [TW] Tensornets pre-trained weight • https://github.com/taehoonlee/tensornets
  • 32. Copyright © 2019 by AI4quant, Inc. [TW] Training Loss v.s. Epoch
  • 33. Copyright © 2019 by AI4quant, Inc. [TW] Model accuracy
  • 34. Copyright © 2019 by AI4quant, Inc. [TW] Metrics- Accuracy
  • 35. Copyright © 2019 by AI4quant, Inc. [TW] MCC and Latency on validation set
  • 36. Copyright © 2019 by AI4quant, Inc. [TW] Learn from the competition report •Latency is the key, Python multi-thread to improve latency •Smaller customized AI model can make the speed even faster
  • 37. Copyright © 2019 by AI4quant, Inc. [TW] 37
  • 38. Copyright © 2019 by AI4quant, Inc. [TW] A Brief Comparison of Edge Computing Devices Ref: https://www.ideas2it.com/blogs/comparison-edge-computing-devices/ 38 Features/De vices Google Coral(Dev Board) Intel Movidius (Compute stick 2) Intel’s UP Squared AI Vision X Developer Kit NVIDIA Jetson (Nano) Raspberry Pi (3 b) CPU Quad Arm Cortex- A53,Cortex-M4F QuadCore 1.6GHz Atom x7-E3950 QuadCore 1.43GHz ARM A57 QuadCore 1.2GHz Broadcom BCM2837 GPU Integrated GC7000 Lite Graphics Integrated Intel HD Graphics 505 128-core Maxwell GPU N/A Dedicated HW for Deep Learning, ANN, CV Google Edge TPU coprocessor 16 Core Myriad X (Opt)16 Core Myriad X N/A N/A
  • 39. Copyright © 2019 by AI4quant, Inc. [TW] High performance & scalability for edge ● Nvidia -> TensorRT ● Intel -> OpenVINO
  • 40. Copyright © 2019 by AI4quant, Inc. [TW] Predictive Maintenance Predictive Modeling System 2017 US patent 2035-05-28 expire 40 Ref: https://patents.google.com/patent/US9699049
  • 41. Copyright © 2019 by AI4quant, Inc. [TW] Thank you! ● Q&A

Editor's Notes

  1. Ask the audience who are one of them
  2. Independent software vendor (ISV) Automated optical inspection (AOI) Computer-integrated manufacturing (CIM)