Application of Neural Networks in Embedded Systems Applications (Ihor Starepravo Technology Stream)
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Transcript of Application of Neural Networks in Embedded Systems Applications (Ihor Starepravo Technology Stream)
![Page 1: Application of Neural Networks in Embedded Systems Applications (Ihor Starepravo Technology Stream)](https://reader031.fdocuments.in/reader031/viewer/2022030317/586fdb531a28ab18428b6093/html5/thumbnails/1.jpg)
Applied Deep-Learning
Computer Vision: Landscape, Capabilities and Case-studies
Programming is an art where artist understands little what was created.
Unknown Author
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A.I. headlines
Google DeepMind software masters the game of Go, takes aim at the world’s top player
GeekWire, January, 2016
A Learning Advance in Artificial Intelligence Rivals Human Abilities
NewYork Times, December, 2015
AI is nearly as good as humans in detecting breast cancer.
Engadget, June, 2016
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Why it is important?
Speech
FinTex
ADAS
Medical
Social
Security
99,98% _______ 10,000K
90% _____ 50K
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Why NOW?
Needs Ways
Hardware
Methods
Tools Data
Use-Cases
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Demand Pyramid
integration
classification
comprehension
Value
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Challenge: Video Comprehension
✔Provide ranking for certain video event • TecVID MED’13 – 16 (Audio, OCR, Speech)
✔Assign action label to video event • Dense trajectory features [Wang, 13]
• CNN features for optical flow [Simonyan, 14]
• 3D convolution networks for videos [Tran, 15]
✔Action localization • Learning to track for spatial-temporal action
localization [Weinzapfel, 15]
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Pros & Cons
Highly flexible
Adaptable to the new tasks
Great for complex noisy data
Deterministic latency
Undebuggable
Compute & power intensive
Large memory footprint
Not quite understood by developers
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What’s different?
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ILSVRC – Architectures Competition
ResNet GoogleNet
VGG
AlexNet
28,2 25,8
16,4
11,7
6,7 7,3
3,57
2010 AlexNet 2011 AlexNet 2012 AlexNet 2013 AlexNet 2014 GoogleNet 2014 VGG 2015 ResNet
layers-> 152
22 19 8 8
5,1
errors% ->
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Dev Landscape: Frameworks
✔Caffee
✔TensorFlow
✔Torch & Co.
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Dev Landscape: Tools
✔NVidia Digits
✔OpenML
✔Proprietary solutions
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CNN Optimizations
✔Fine tuning – learning optimization
✔Nets pruning – less memory footprint
✔Forward speed – decrease latency
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Architectures Performance
CNNs Alex Net
Google Net
VGG-19 VGG-16 SqueezeNet
ResNet-152
Leaning time, hours 187 673 4100 3500 n.a 2680
Forward, sec 0,3 1 4 3,5 0,3 ?
Weights, MB 230 51 548 528 4,7 230
Top-5 error, % 16,4 6,7 7,3 7,4 16,4 3,57
Number of layers 8 22 19 16 65 152
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Machine Learning: Misconceptions
No self-learning
No universal architecture
Spatial-temporal analysis
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Examples
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AlexNet at ARM Mali T760
AlexNet@CAFFEE image visual analysis. Creates tags and understand your picture offline
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DNN for ADAS
Driver assist systems leverage DNNs to classify road obstacles. Hybrid HOG + DNN
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YOLO + DNN + Dense Trajectories
Specialized video surveillance to achieve unprecedented value for pharma. Provides reliability and accuracy of video data. Example of visual cognition.
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Lessons Learned:
Deep Learning is in it’s infancy
Be ready for chaotic tools landscape
Don’t break up with traditional algorithmic approach
Be ready to change your mindset!
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Thank you!
Questions?
Programming is an art where artist understands little what was created.
Unknown Author