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GÖMÜLÜ SİSTEMLERDE
DERİN ÖĞRENME UYGULAMALARI
Ferhat Kurt https://embedded.openzeka.com
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Microsoft & Google
“Superhuman” Image
Recognition
Microsoft
“Super Deep Network”
Berkeley’s Brett
End-to-End
Reinforcement Learning
Deep Speech 2
One network, 2 languages
A New Computing Model
Hits Pop Culture
AlphaGo
Rivals a World Champion
TU Delft Deep-Learning
Amazon Picking Champion
YAPAY ZEKA KİLOMETRE TAŞLARI
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Deep Learning and
Computer Vision
Graphics GPU Compute
NVIDIA GPU: GRAFİKTEN DAHA FAZLASI
GPU'lar üstün
performans ve
verimlilik sunar
Tümleşik algılama
ve derin öğrenme,
otonomluk sağlar
x1
x2
x3
x4
OTONOM MAKİNELERİN YÜKSELİŞİ
Otonomluk
gerektiren yeni
kullanım durumları
ÖNCÜ JETSON TEKNOLOJİSİ
Otonom Makinelerin Gelecek Nesline Güç Veriyor
Jetson TX1
Bir Modül Üzerinde Süper Bilgisayar
10 W altında benzersiz performans
Otonom makineler için gelişmiş teknoloji
Kredi kartından daha küçük
JETSON TX1
GPU 1 TFLOP/s 256-core Maxwell
CPU 4x 64-bit ARM A57 CPUs | 1.6GHz
Memory 4 GB LPDDR4 | 25.6 GB/s
Video decode 4K 60Hz H.264
Video encode 4K 30Hz H.264
CSI Up to 6 cameras | 1400 Mpix/s
Display 2x DSI, 1x eDP 1.4, 1x DP 1.2/HDMI
Wi-Fi 802.11 2x2 ac
Networking 1 Gigabit Ethernet
PCI-E Gen 2 1x1 + 1x4
Storage 16 GB eMMC, SDIO, SATA
Other 3x UART, 3x SPI, 4x I2C, 4x I2S, GPIOs
Power 10-15W, 6.6V-19.5VDC
Size 50mm x 87mm
Modül Üstünde Sistem
Jetson TX1
Developer Kit
Jetson TX1
Developer Board
5MP Camera
DIGITS Workflow VisionWorks Jetson Multimedia SDK
ve diğer teknolojiler:
CUDA, Linux4Tegra, NSIGHT EE, OpenCV4Tegra,
OpenGL, Vulkan, System Trace, Visual Profiler, Ubuntu 14.04
Deep Learning SDK
NVIDIA JETPACK
Linux for
Tegra
Compute
(CUDA)
Jetson TX1
Vision
Machine
Learning
cuSPARSE
cuSolver
cuFFT
cuBLAS NPP
cuRAND Thrust
CUDA Math Library
Graphics
Araçlar
NVTX
NVIDIA Tools eXtension
Source
code editor
Debugger
Profiler
System
Trace
Dikey Entegre Edilmiş Paketler
V4L2
libjpeg
JETSON SDK: DETAYLAR
VISIONWORKS™
CUDA-accelerated Computer Vision Toolkit
• Full OpenVX 1.1 implementation
• Easy integration with existing CV pipelines
• Custom extensions
Applications
VisionWorks
CUDA
Jetson TX1
VisionWorks™
Toolkit
Robotics Augmented
Reality Drones
Example
Applications
Feature
Tracking
Structure
from Motion
Object
Tracking
Dense
Optical Flow
VisionWorks™ API + FrameWorks
IMAGE ARITHMETIC
AbsoluteDifference
AccumulateImage
Accumulate Squared
Accumulate Weighted
Add / Subtract / Multiply
Channel Combine
ChannelExtract
GEOMETRIC
TRANSFORMS
Affine Warp +
Perspective Warp
Flip Image
Gaussian Pyramid
Remap
Scale Image
Features
Canny EdgeDetector
Fast Corners+
Fast Track
Harris Corners +
HarrisTrack
HoughCircles
HoughLines
• Jetpack SDK
• Libraries
• Developer tools
• Design collateral
• Developer Forum
• Training and Tutorials
• Ecosystem
http://developer.nvidia.com/embedded-computing
Kapsamlı Geliştirici Platformu
GETTING
STARTED
JETSON COMMUNITY
Developer Forums devtalk.nvidia.com eLinux Wiki eLinux.org/Jetson_TX1
• Infrared devices:
• SICK LIDAR (LMS 200); Hokuyo; rpLIDAR
• Asus Xtion Pro Live (PrimeSense)
• Intel RealSense (mult. generations)
• Stereo and color cameras:
• StereoLabs Zed (consumer-oriented)
• Point Grey Research USB3 and GigE
• e-con Systems CSI-MIPI Cameras
with external ISP
THE PERIPHERALS JETSON
CONNECTS WITH
including Community Contributions
JETSON TX1 MODÜLÜ YERLEŞTİRME
Modüler Ekosistem
• ConnectTech Orbitty
• ConnectTech Rosie
• Auvidea J120
• Colorado Engineering
TX1-SOM TX1 MODÜL
GPU Inference Engine
ile Gerçek Zamanlı Derin
Öğrenme Ağlarını
Uygulama
72%
74%
84%
88%
93%
96.4%
Human:94.9%
2010 2011 2012 20152013 2014
GPU’da Derin Öğrenme
OTONOMA NE KADAR UZAĞIZ?
ImageNet sınıflandırma doğruluğu
DERİN ÖĞRENME
Fark Ne?
Derin Öğrenme
DNN + Veri + HPC
Geleneksel Bilgisayarlı Görü
Uzman + Zaman
YENİ HESAPLAMA MODELİ
Otonom Makienler
Onboard Zeka
Nesne Sınıflandırma
Segmentasyon
Çarpışma Önleme
3D Geriçatma
Lokalizasyon/
Haritalandırma
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
POWERING THE DEEP LEARNING ECOSYSTEM
NVIDIA SDK Accelerates Every Major Framework
developer.nvidia.com/deep-learning-software
DEEP LEARNING FRAMEWORKS
COMPUTER VISION SPEECH ANDAUDIO NATURAL LANGUAGE PROCESSING
Object Detection Voice Recognition Language Translation
Recommendation
Engines
Sentiment Analysis
Mocha.jl
Image Classification
NVIDIA DEEP LEARNING SDK
NCCLcuDNN cuBLAS GIEcuSPARSE
A COMPLETE COMPUTE PLATFORM
MANAGE TRAIN DEPLOY
DIGITS
DATACENTER AUTOMOTIVE
TRAINTEST
MANAGE /AUGMENT
EMBEDDED
GPU INFERENCE ENGINE
NVIDIA DIGITS
Test Image
developer.nvidia.com/digits
input
concat
İnteraktif Derin Öğrenme GPU Eğitim Sistemi
Veri İşleme DNN Yapılandırma İşlem Görüntüleme Görselleştirme
FIRST Team 900
ROBUST DATA
COLLECTION
ZEBRACORNS
team900.org
GPU INFERENCE ENGINE
Workflow
DIGITS OPTIMIZATION
ENGINE
EXECUTION
ENGINE
PLANNEURAL
NETWORK
input
concatdeveloper.nvidia.com/gpu-inference-engine
NVIDIA GPU Inference Engine (GIE)
provides even higher efficiency and
performance for neural network
inference.
Tests performed using GoogLenet.
CPU-only: Single-socket Intel Xeon
(Haswell) E5-2698 v3@2.3GHz with
HT.
GPU: NVIDIA Tesla M4 + cuDNN 5 RC.
GPU + GIE: NVIDIA Tesla M4 + GIE.
input
concat
GPU INFERENCE ENGINE
Optimizations
• Fuse network layers
• Eliminate concatenation layers
• Kernel specialization
• Auto-tuning for target platform
• Select optimal tensor layout
• Batch size tuningTRAINED
NEURALNETWORK
input
concat
OPTIMIZED
INFERENCE
RUNTIME
developer.nvidia.com/gpu-inference-engine
Graph Optimization
concat
max pool
next input
1x1 conv.
relu
bias
relu
bias
1x1 conv.
relu
bias
3x3 conv.
relu
bias
1x1 conv.
relu
bias
5x5 conv.
relu
bias
1x1 conv.
input
concat
Graph Optimization
Vertical fusion
max pool
input
concat
next input
concat
1x1 CBR 3x3 CBR 5x5 CBR 1x1 CBR
1x1 CBR 1x1 CBR
Graph Optimization
Horizontal fusion
concat
max pool
next input
3x3 CBR 5x5 CBR 1x1 CBR
1x1 CBR
input
concat
Graph Optimization
Concat elision
max pool
input
next input
3x3 CBR 5x5 CBR 1x1 CBR
1x1 CBR
• Baseline is cuDNN / cuBLAS
• Direct convolution kernels for small batch
• Custom Winograd & Implicit GEMM for Half2
• Custom Deconvolution for filter size == stride case
• Weight pre-transform for Winograd
• Optimal T/N choice for BLAS
• Run cudnnFindForwardConvolutionEx() with multiple iterations
Autotuning
Choose the fastest kernel for each layer
// create the network definition
INetworkDefinition* network = infer->createNetwork();
// create a map from caffe blob names to GIE tensors
std::unordered_map<std::string, infer1::Tensor> blobNameToTensor;
// populate the network definition and map
CaffeParser* parser = new CaffeParser;
parser->parse(deployFile, modelFile, *network, blobNameToTensor);
// tell GIE which tensors are required outputs
for (auto& s : outputs)
network->setOutput(blobNameToTensor[s]);
Build
Importing a Caffe Model
// Specify the maximum batch size and scratch size
CudaEngineBuildContext buildContext;
buildContext.maxBatchSize = maxBatchSize;
buildContext.maxWorkspaceSize = 1 << 20;
// create the engine
ICudaEngine* engine =
infer->createCudaEngine(buildContext, *network);
// serialize to a C++ stream
engine->serialize(gieModelStream);
Build
Engine Creation
// get array bindings for input and output
int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME),
outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
// set array of input and output buffers
void* buffers[2];
buffers[inputIndex] = gpuInputBuffer;
buffers[outputIndex] = gpuOutputBuffer;
Runtime
Binding Buffers
// Specify the batch size
CudaEngineContext context;
context.batchSize = batchSize;
// add GIE kernels to the given stream
engine->enqueue(context, buffers, stream, NULL);
<…>
// wait on the stream
cudaStreamSynchronize(stream);
Runtime
Running the Engine
Training organizations and individuals to solve challenging problems using Deep Learning
On-site workshops and online courses presented by certified experts
Covering complete workflows for proven application use cases
Image classification, object detection, natural language processing, recommendation systems, and more
www.nvidia.com/dli
Hands-on Training for Data Scientists and Software Engineers
NVIDIA Deep Learning Institute
Deep Reinforcement Learning
PLAYING ATARI WITH
DEEPMIND
From Pixels to Actions: Human-level control through
Deep Reinforcement Learning
http://arxiv.org/abs/1602.01783
http://arxiv.org/abs/1602.01783
Inside Google’s DeepMind
AlphaGo GPU cluster
END-TO-END LEARNING
Motor PWM
Sensory Inputs
Perceptron
RNN
Recognition
Inference
Goal/Reward
user
task
Short-termLong-term
MOTION CONTROL
AUTONOMOUS NAVIGATION
49
OpenAI Gym
Gazebo
Unreal4Torch
PhysX
Others
SIMULATION
Physical Intuition
A reinforcement learning agent includes:
state (environment)
actions (controls)
reward (feedback)
A value function predicts the future reward
of performing actions in the current state
Given the recent state, action with the maximum
estimated future reward is chosen for execution
For agents with complex state spaces, deep
networks are used as Q-value approximator
Numerical solver (gradient descent) optimizes
the network on-the-fly based on reward inputs
Q-LEARNING
How’s it work?
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
LSTM ACCELERATION
Launch a 2D grid of RNN cells
Multiple layers in a single call are faster
Doesn’t suffer from vanishing gradient
Able to adopt long-term strategy
Supports:
Partially-observable environments
Uni/Bidirectional RNNs
Non-uniform length minibatches
Dropout between layers
DEEP-LEARNING RESEARCH ROVER
TURBO 2.0
github.org/dusty-nv
Derin Öğrenme Sunucuları
(Kütüphane, veri setleri, ağ yapısı ve modellerini içerir)
İstek ön işleme ve sonuç döndürme katmanı
Kullanıcı arayüzü (Web+Api desteği)
Görüntü
Analizi
Ses analizi Veri analizi
Müşteriye özel
analiz yapısı
Girdi Çıktı
Resim
Video
Ses (sinyal)
Veri
Gerçek zamanlı
Sınıflandırılmış
veya
anlamlandırılmış
çıktı
Open Zeka Mimarisi
Open Zeka API
GPU ve CPU Bulutu Üzerinde Gömülü Sistemler
Jetson TX1-TK1
Rasberry Pi 3
Test devam ediyor
Frame Dönüşümü Ses Ayrışımı
Resim ne
anlatıyor?
Ses ne anlatıyor?
Görüntü
Kaynaklar Tür Model
Fotoğraf
Video
frame
RGB
Termal
(LWIR/SWI
R)
Monochrom
e
Nesne
Tespiti
Yüz tanıma
Konsept
Konsept
MSI/HSI
Ses
Metin/Veri
Veri
Open Zeka Servisi
Son kullanıcıya Cloud üzerinde
insan algısına yakın bir seviyede
görüntü, ses ve veri analizi
sunma
Model barındırma servisi
(Geliştirici arayüz desteği)
Algoritma geliştirme ve barındırma
servisi (Esnek mimari)
Nerede Kullanılacak
• Kamera görüntülerinin (Resim-akış) gerçek
zamanlı anlamlandırılması,
• Eğlence sektörü,
• Sürücü destek sistemleri,
• Otonom ve robotik sistemler (Gömülü teknoloji)
• Savunma sanayiinde sensör kullanan mimarilere
yapay zekâ kazandırılması (Karar destek sistemi)
• Sağlık alanında görüntü ve veri analizi
• Büyük veri analizi (Finans)
Güvenlik
kameralarının
bulut içerisinde
gerçek zamanlı
analizi
Open Zeka Jetson TX1 Türkiye tedarikçisidir.
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Gömülü Sistemlerde Derin Öğrenme Uygulamaları
Türkiye Derin Öğrenme Grubu Sayfası: https://www.linkedin.com/grp/home?gid=8334641
Ankara Derin Öğrenme Meetup Sayfası: http://www.meetup.com/Ankara-Deep-Learning
Derin Öğrenme Grup Sayfası: https://www.facebook.com/groups/derin.ogrenme
http://www.derinogrenme.com
“If we knew what it was we were doing, it
would not be called research, would it?”
Einstein
TEŞEKKÜRLER.

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Gömülü Sistemlerde Derin Öğrenme Uygulamaları

  • 1. GÖMÜLÜ SİSTEMLERDE DERİN ÖĞRENME UYGULAMALARI Ferhat Kurt https://embedded.openzeka.com
  • 3. Microsoft & Google “Superhuman” Image Recognition Microsoft “Super Deep Network” Berkeley’s Brett End-to-End Reinforcement Learning Deep Speech 2 One network, 2 languages A New Computing Model Hits Pop Culture AlphaGo Rivals a World Champion TU Delft Deep-Learning Amazon Picking Champion YAPAY ZEKA KİLOMETRE TAŞLARI
  • 5. Deep Learning and Computer Vision Graphics GPU Compute NVIDIA GPU: GRAFİKTEN DAHA FAZLASI
  • 6. GPU'lar üstün performans ve verimlilik sunar Tümleşik algılama ve derin öğrenme, otonomluk sağlar x1 x2 x3 x4 OTONOM MAKİNELERİN YÜKSELİŞİ Otonomluk gerektiren yeni kullanım durumları
  • 7. ÖNCÜ JETSON TEKNOLOJİSİ Otonom Makinelerin Gelecek Nesline Güç Veriyor
  • 8. Jetson TX1 Bir Modül Üzerinde Süper Bilgisayar 10 W altında benzersiz performans Otonom makineler için gelişmiş teknoloji Kredi kartından daha küçük
  • 9. JETSON TX1 GPU 1 TFLOP/s 256-core Maxwell CPU 4x 64-bit ARM A57 CPUs | 1.6GHz Memory 4 GB LPDDR4 | 25.6 GB/s Video decode 4K 60Hz H.264 Video encode 4K 30Hz H.264 CSI Up to 6 cameras | 1400 Mpix/s Display 2x DSI, 1x eDP 1.4, 1x DP 1.2/HDMI Wi-Fi 802.11 2x2 ac Networking 1 Gigabit Ethernet PCI-E Gen 2 1x1 + 1x4 Storage 16 GB eMMC, SDIO, SATA Other 3x UART, 3x SPI, 4x I2C, 4x I2S, GPIOs Power 10-15W, 6.6V-19.5VDC Size 50mm x 87mm Modül Üstünde Sistem
  • 10. Jetson TX1 Developer Kit Jetson TX1 Developer Board 5MP Camera
  • 11. DIGITS Workflow VisionWorks Jetson Multimedia SDK ve diğer teknolojiler: CUDA, Linux4Tegra, NSIGHT EE, OpenCV4Tegra, OpenGL, Vulkan, System Trace, Visual Profiler, Ubuntu 14.04 Deep Learning SDK NVIDIA JETPACK
  • 12. Linux for Tegra Compute (CUDA) Jetson TX1 Vision Machine Learning cuSPARSE cuSolver cuFFT cuBLAS NPP cuRAND Thrust CUDA Math Library Graphics Araçlar NVTX NVIDIA Tools eXtension Source code editor Debugger Profiler System Trace Dikey Entegre Edilmiş Paketler V4L2 libjpeg JETSON SDK: DETAYLAR
  • 13. VISIONWORKS™ CUDA-accelerated Computer Vision Toolkit • Full OpenVX 1.1 implementation • Easy integration with existing CV pipelines • Custom extensions Applications VisionWorks CUDA Jetson TX1 VisionWorks™ Toolkit Robotics Augmented Reality Drones Example Applications Feature Tracking Structure from Motion Object Tracking Dense Optical Flow VisionWorks™ API + FrameWorks IMAGE ARITHMETIC AbsoluteDifference AccumulateImage Accumulate Squared Accumulate Weighted Add / Subtract / Multiply Channel Combine ChannelExtract GEOMETRIC TRANSFORMS Affine Warp + Perspective Warp Flip Image Gaussian Pyramid Remap Scale Image Features Canny EdgeDetector Fast Corners+ Fast Track Harris Corners + HarrisTrack HoughCircles HoughLines
  • 14. • Jetpack SDK • Libraries • Developer tools • Design collateral • Developer Forum • Training and Tutorials • Ecosystem http://developer.nvidia.com/embedded-computing Kapsamlı Geliştirici Platformu
  • 15. GETTING STARTED JETSON COMMUNITY Developer Forums devtalk.nvidia.com eLinux Wiki eLinux.org/Jetson_TX1
  • 16. • Infrared devices: • SICK LIDAR (LMS 200); Hokuyo; rpLIDAR • Asus Xtion Pro Live (PrimeSense) • Intel RealSense (mult. generations) • Stereo and color cameras: • StereoLabs Zed (consumer-oriented) • Point Grey Research USB3 and GigE • e-con Systems CSI-MIPI Cameras with external ISP THE PERIPHERALS JETSON CONNECTS WITH including Community Contributions
  • 17. JETSON TX1 MODÜLÜ YERLEŞTİRME Modüler Ekosistem • ConnectTech Orbitty • ConnectTech Rosie • Auvidea J120 • Colorado Engineering TX1-SOM TX1 MODÜL
  • 18. GPU Inference Engine ile Gerçek Zamanlı Derin Öğrenme Ağlarını Uygulama
  • 19. 72% 74% 84% 88% 93% 96.4% Human:94.9% 2010 2011 2012 20152013 2014 GPU’da Derin Öğrenme OTONOMA NE KADAR UZAĞIZ? ImageNet sınıflandırma doğruluğu
  • 21. Derin Öğrenme DNN + Veri + HPC Geleneksel Bilgisayarlı Görü Uzman + Zaman YENİ HESAPLAMA MODELİ Otonom Makienler Onboard Zeka
  • 22. Nesne Sınıflandırma Segmentasyon Çarpışma Önleme 3D Geriçatma Lokalizasyon/ Haritalandırma
  • 24. POWERING THE DEEP LEARNING ECOSYSTEM NVIDIA SDK Accelerates Every Major Framework developer.nvidia.com/deep-learning-software DEEP LEARNING FRAMEWORKS COMPUTER VISION SPEECH ANDAUDIO NATURAL LANGUAGE PROCESSING Object Detection Voice Recognition Language Translation Recommendation Engines Sentiment Analysis Mocha.jl Image Classification NVIDIA DEEP LEARNING SDK NCCLcuDNN cuBLAS GIEcuSPARSE
  • 25. A COMPLETE COMPUTE PLATFORM MANAGE TRAIN DEPLOY DIGITS DATACENTER AUTOMOTIVE TRAINTEST MANAGE /AUGMENT EMBEDDED GPU INFERENCE ENGINE
  • 26. NVIDIA DIGITS Test Image developer.nvidia.com/digits input concat İnteraktif Derin Öğrenme GPU Eğitim Sistemi Veri İşleme DNN Yapılandırma İşlem Görüntüleme Görselleştirme
  • 27. FIRST Team 900 ROBUST DATA COLLECTION ZEBRACORNS team900.org
  • 28. GPU INFERENCE ENGINE Workflow DIGITS OPTIMIZATION ENGINE EXECUTION ENGINE PLANNEURAL NETWORK input concatdeveloper.nvidia.com/gpu-inference-engine
  • 29. NVIDIA GPU Inference Engine (GIE) provides even higher efficiency and performance for neural network inference. Tests performed using GoogLenet. CPU-only: Single-socket Intel Xeon (Haswell) E5-2698 v3@2.3GHz with HT. GPU: NVIDIA Tesla M4 + cuDNN 5 RC. GPU + GIE: NVIDIA Tesla M4 + GIE.
  • 31. GPU INFERENCE ENGINE Optimizations • Fuse network layers • Eliminate concatenation layers • Kernel specialization • Auto-tuning for target platform • Select optimal tensor layout • Batch size tuningTRAINED NEURALNETWORK input concat OPTIMIZED INFERENCE RUNTIME developer.nvidia.com/gpu-inference-engine
  • 32. Graph Optimization concat max pool next input 1x1 conv. relu bias relu bias 1x1 conv. relu bias 3x3 conv. relu bias 1x1 conv. relu bias 5x5 conv. relu bias 1x1 conv. input concat
  • 33. Graph Optimization Vertical fusion max pool input concat next input concat 1x1 CBR 3x3 CBR 5x5 CBR 1x1 CBR 1x1 CBR 1x1 CBR
  • 34. Graph Optimization Horizontal fusion concat max pool next input 3x3 CBR 5x5 CBR 1x1 CBR 1x1 CBR input concat
  • 35. Graph Optimization Concat elision max pool input next input 3x3 CBR 5x5 CBR 1x1 CBR 1x1 CBR
  • 36. • Baseline is cuDNN / cuBLAS • Direct convolution kernels for small batch • Custom Winograd & Implicit GEMM for Half2 • Custom Deconvolution for filter size == stride case • Weight pre-transform for Winograd • Optimal T/N choice for BLAS • Run cudnnFindForwardConvolutionEx() with multiple iterations Autotuning Choose the fastest kernel for each layer
  • 37. // create the network definition INetworkDefinition* network = infer->createNetwork(); // create a map from caffe blob names to GIE tensors std::unordered_map<std::string, infer1::Tensor> blobNameToTensor; // populate the network definition and map CaffeParser* parser = new CaffeParser; parser->parse(deployFile, modelFile, *network, blobNameToTensor); // tell GIE which tensors are required outputs for (auto& s : outputs) network->setOutput(blobNameToTensor[s]); Build Importing a Caffe Model
  • 38. // Specify the maximum batch size and scratch size CudaEngineBuildContext buildContext; buildContext.maxBatchSize = maxBatchSize; buildContext.maxWorkspaceSize = 1 << 20; // create the engine ICudaEngine* engine = infer->createCudaEngine(buildContext, *network); // serialize to a C++ stream engine->serialize(gieModelStream); Build Engine Creation
  • 39. // get array bindings for input and output int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME), outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME); // set array of input and output buffers void* buffers[2]; buffers[inputIndex] = gpuInputBuffer; buffers[outputIndex] = gpuOutputBuffer; Runtime Binding Buffers
  • 40. // Specify the batch size CudaEngineContext context; context.batchSize = batchSize; // add GIE kernels to the given stream engine->enqueue(context, buffers, stream, NULL); <…> // wait on the stream cudaStreamSynchronize(stream); Runtime Running the Engine
  • 41. Training organizations and individuals to solve challenging problems using Deep Learning On-site workshops and online courses presented by certified experts Covering complete workflows for proven application use cases Image classification, object detection, natural language processing, recommendation systems, and more www.nvidia.com/dli Hands-on Training for Data Scientists and Software Engineers NVIDIA Deep Learning Institute
  • 43. PLAYING ATARI WITH DEEPMIND From Pixels to Actions: Human-level control through Deep Reinforcement Learning
  • 46. END-TO-END LEARNING Motor PWM Sensory Inputs Perceptron RNN Recognition Inference Goal/Reward user task Short-termLong-term MOTION CONTROL AUTONOMOUS NAVIGATION
  • 48. A reinforcement learning agent includes: state (environment) actions (controls) reward (feedback) A value function predicts the future reward of performing actions in the current state Given the recent state, action with the maximum estimated future reward is chosen for execution For agents with complex state spaces, deep networks are used as Q-value approximator Numerical solver (gradient descent) optimizes the network on-the-fly based on reward inputs Q-LEARNING How’s it work?
  • 50. LSTM ACCELERATION Launch a 2D grid of RNN cells Multiple layers in a single call are faster Doesn’t suffer from vanishing gradient Able to adopt long-term strategy Supports: Partially-observable environments Uni/Bidirectional RNNs Non-uniform length minibatches Dropout between layers
  • 51. DEEP-LEARNING RESEARCH ROVER TURBO 2.0 github.org/dusty-nv
  • 52. Derin Öğrenme Sunucuları (Kütüphane, veri setleri, ağ yapısı ve modellerini içerir) İstek ön işleme ve sonuç döndürme katmanı Kullanıcı arayüzü (Web+Api desteği) Görüntü Analizi Ses analizi Veri analizi Müşteriye özel analiz yapısı Girdi Çıktı Resim Video Ses (sinyal) Veri Gerçek zamanlı Sınıflandırılmış veya anlamlandırılmış çıktı Open Zeka Mimarisi
  • 53. Open Zeka API GPU ve CPU Bulutu Üzerinde Gömülü Sistemler Jetson TX1-TK1 Rasberry Pi 3 Test devam ediyor
  • 54. Frame Dönüşümü Ses Ayrışımı Resim ne anlatıyor? Ses ne anlatıyor?
  • 56. Open Zeka Servisi Son kullanıcıya Cloud üzerinde insan algısına yakın bir seviyede görüntü, ses ve veri analizi sunma Model barındırma servisi (Geliştirici arayüz desteği) Algoritma geliştirme ve barındırma servisi (Esnek mimari)
  • 57. Nerede Kullanılacak • Kamera görüntülerinin (Resim-akış) gerçek zamanlı anlamlandırılması, • Eğlence sektörü, • Sürücü destek sistemleri, • Otonom ve robotik sistemler (Gömülü teknoloji) • Savunma sanayiinde sensör kullanan mimarilere yapay zekâ kazandırılması (Karar destek sistemi) • Sağlık alanında görüntü ve veri analizi • Büyük veri analizi (Finans) Güvenlik kameralarının bulut içerisinde gerçek zamanlı analizi
  • 58. Open Zeka Jetson TX1 Türkiye tedarikçisidir.
  • 68. Türkiye Derin Öğrenme Grubu Sayfası: https://www.linkedin.com/grp/home?gid=8334641 Ankara Derin Öğrenme Meetup Sayfası: http://www.meetup.com/Ankara-Deep-Learning Derin Öğrenme Grup Sayfası: https://www.facebook.com/groups/derin.ogrenme http://www.derinogrenme.com
  • 69. “If we knew what it was we were doing, it would not be called research, would it?” Einstein TEŞEKKÜRLER.