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GTC Taiwan 2017 在 Google Cloud 當中使用 GPU 進行效能最佳化
GTC Taiwan 2017 在 Google Cloud 當中使用 GPU 進行效能最佳化
GTC Taiwan 2017 NVIDIA VRWorks SDK 加速性能與提升 VR 使用經驗
GTC Taiwan 2017 NVIDIA VRWorks SDK 加速性能與提升 VR 使用經驗
GTC Taiwan 2017 NVIDIA Holodeck 與 Isaac VR 技術分享
GTC Taiwan 2017 NVIDIA Holodeck 與 Isaac VR 技術分享
GTC Taiwan 2017 深度學習於表面瑕疵檢測之應用
GTC Taiwan 2017 深度學習於表面瑕疵檢測之應用
GTC Taiwan 2017 結合智能視覺系統之機械手臂
GTC Taiwan 2017 結合智能視覺系統之機械手臂
GTC Taiwan 2017 以雲端 GPU 將傳統硬體人工智慧化
GTC Taiwan 2017 以雲端 GPU 將傳統硬體人工智慧化
GTC Taiwan 2017 GPU 平台上導入深度學習於半導體產業之 EDA 應用
GTC Taiwan 2017 GPU 平台上導入深度學習於半導體產業之 EDA 應用
GTC Taiwan 2017 深度學習與該技術於視訊監控產業上之應用
GTC Taiwan 2017 深度學習與該技術於視訊監控產業上之應用
GTC Taiwan 2017 企業端深度學習與人工智慧應用
GTC Taiwan 2017 企業端深度學習與人工智慧應用
GTC Taiwan 2017 應用智慧科技於傳染病防治
GTC Taiwan 2017 應用智慧科技於傳染病防治
NVIDIA深度學習教育機構 (DLI): Deep Learning Institute
NVIDIA深度學習教育機構 (DLI): Deep Learning Institute
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NVIDIA深度學習教育機構 (DLI): Object detection with jetson
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Medical Image Processing on NVIDIA TK1/TX1
1.
TAIPEI | SEP.
21-22, 2016 Che-Lun Hung, 2016/9/21 MEDICAL IMAGE PROCESSING ON NVIDIA TK1/TX1
2.
2 AGENDA Medical Image Brain MRI
Image Segmentation Fuzzy C-Means Method on TK1/TX1 Genetic Fuzzy C-Means Method on Multiple TK1s
3.
3 MEDICAL IMAGE
4.
4 MEDICAL IMAGE X-Ray Ultrasound Image resource
: wiki CT MRI Image Source: https://en.wikipedia.org/
5.
5 WHY NEED MEDICAL
IMAGE PROCESSING? 9/26/16 Computer-aided diagnosis Description of Lension Image Source: http://www.dailyherald.com/ Image Source: http://artificialintelligencefordummies.weebly.com/
6.
6 TK1/TX1 TK1 Kepler 192 CUDA cores 2G
Memory TX1 Maxwell 256 CUDA cores 4G Memory
7.
7 BRAIN MRI IMAGE
SEGMENTATION
8.
8 BRAIN MRI IMAGE Image
Source: http://brainweb.bic.mni.mcgill.ca/brainweb/
9.
9 SEGMENTATION Image Source: http://brainweb.bic.mni.mcgill.ca/brainweb/
10.
10 CLUSTERING METHODOLOGY
11.
11 SEGMENTATION FOR BRAIN
MRI
12.
12 FUZZY C-MEANS METHOD
ON TK1/TX1
13.
13 FUZZY C-MEANS METHOD Jm
= uij n d xi,θj( ) j=1 c ∑ i=1 n ∑
14.
14 EXPERIMENTAL PLATFORMS TK1/TX1 The host
(CPU) Intel Xeon E3-1231 v3 3.40GHz with 64GB RAM Data Size 1150*1280 9/26/16
15.
15 SEGMENTATION RESULTS Image Source:
http://brainweb.bic.mni.mcgill.ca/brainweb/
16.
16 PERFORMANCE 1 7.2 12.9 0 2 4 6 8 10 12 14 Intel E3 CPU TK1 GPU TX1 Speedup Platform IMG
Size= 1150*1280
17.
17 DIFFERENT PRECISION FORMAT 5.93 2.22 3.81 1.42 0 1 2 3 4 5 6 7 TK1 GPU double
TK1 GPU float TX1 GPU double TX1 GPU float Time(seconds) Platform
18.
18 TK1/TX1 MAXIMUM PERFORMANCE
MODE 5.93 2.22 3.81 1.42 5.45 2.15 3.69 1.39 0 1 2 3 4 5 6 7 TK1 GPU double TK1 GPU float TX1 GPU double TX1 GPU float Time(seconds) Platform Kernel time(Normal) Kernel time(HP)
19.
19 DIFFERENT MEMORY COPY
MODES 2.15 1.39 2.38 1.5 2.37 1.45 0 0.5 1 1.5 2 2.5 TK1 GPU float TX1 GPU float Time(s) Platform Kernel time Kernel time(Zero-copy) Kernel time(Unified memory)
20.
20 GENETIC FUZZY C-MEANS
METHOD ON MULTIPLE TK1S
21.
21 GENETIC FUZZY C-MEANS
METHOD Image Source: http://http://www.turingfinance.com/
22.
22 MESSAGE PASSING INTERFACE
(MPI) Image Source: https://computing.llnl.gov/tutorials/mpi/
23.
23 MULTIPLE GPU MPI+CUDA
24.
24 EXPERIMENTAL PLATFORMS TK1/TX1 The hosts
(CPU) Intel Xeon E5-2620 2.5GHz, E3-1231 V3 3.4GHz, and I7-4280K 3.7GHz. Data Size 1150*1280 9/26/16
25.
25 DIFFERENT POPULATION SIZES (a)
(b) (c) (d) (e) (f) (a) population size = 30, (b) population size = 60, (c) population size = 90, (d) population size = 120, (e) population size = 150, (f) population size = 180. Image Source: http://brainweb.bic.mni.mcgill.ca/brainweb/
26.
26 PERFORMANCE
27.
27 SUITABLE FOR ALL
MEDICAL IMAGE PROCESSING TECHNOLOGIES?
28.
28 MAMMOGRAPHIC IMAGE
29.
29 TEXTURE-BASED IMAGE PROCESSING
(1)
30.
30 TEXTURE-BASED IMAGE PROCESSING
(2) 127*127127*127127*127127*127 ……………127*127 127 * 127 threads
31.
31 EXPERIMENTAL PLATFORMS TK1/TX1 The host
(CPU) Intel Xeon E3-1231 v3 3.40GHz with 64GB RAM Data Size 1123*1751 9/26/16
32.
32 PERFORMANCE 0 5000 10000 15000 20000 25000 30000 35000 Matlab with NVIDIA GTX
980 Matlab with Intel Xeon E3-1231 NVIDIA TX1 NVIDIA TK1 550 1752 20260 31469 Executiontime(s) Device
33.
33 MORE POSSIBILITIES FOR
GPU ON MEDICAL IMAGE PROCESSING
34.
TAIPEI | SEP.
21-22, 2016 THANK YOU
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