The latest and greatest - NVIDIA · 2016. 4. 10. · An intersection of technology and trends: •...
Transcript of The latest and greatest - NVIDIA · 2016. 4. 10. · An intersection of technology and trends: •...
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Hugo Latapie / Enzo Fenoglio
Santosh Pandey, JP Vasseur, Rob Liston, Dan Tan, Xiaoqing Zhu, John Apostolopoulos
with the newly formed IoE Video Analytics virtual team
At the edge of the network
Deep Learning
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• Audio/video metadata extraction
• Multi-modal TV UX
• Encrypted Network Traffic Classification
• Video compression
HPC Since 2011
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Public/Private
Internet
DLA DLA
Branch 1
SLN Architecture
Internet
Branch 2
Controller
ISE
SCA Threat
Intel
Threat Grid,
OpennDNS,
WBRS, ... Other
TI feeds
• Sensing (knowledge): granular data
collection with knowledge extraction from
NetFlow but also Deep Packet Inspection
on control and data plane & local states
• Machine Learning: real-time embedded
behavioral modeling and anomaly
detection
• Control: autonomous embedded control,
advanced networking control (police,
shaper, recoloring, redirect, ...)
DL
A
• Orchestration of Distributed Learning
Agents (DLAs)
• Advanced Visualization of anomalies
• Centralized policy for mitigation
• Interaction with other security components
such as ISE and Threat Intelligence Feeds
• North bound API to SIEM/Database (e.g.
Splunk) using CEF/CIM format
• Evaluation of anomaly relevancy
SC
A
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Evolution of location use-cases
• Healthcare Asset management & wayfinding
• Retail Engage shopper in aisle & deliver proximity-based offers
• Museum Enabling the Digital docent
• Office Wayfinding & workspace optimization
93%
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• State of the art: • 5-7m accuracy
• Multilateration on WiFi Client based on RSSI at multiple APs
• Cisco-Hyperlocation: • 1m accuracy
• Increase accuracy & reliability
• AP connected clients
• Not applicable to non-connected/probing clients or tags
• Add Angle-of-Arrival in addition to RSSI
• Introduce enhanced location with 1AP
Technology – network based location
- RSSI Receive Signal Strength Indicator
- ToF Time of Flight
- DRTT Differential Round-trip-time
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How Hyperlocation Works Built on Cisco Unified Access
CM
X A
na
lyti
cs
Controller
(Virtual/Physical) MSE
(Virtual/Physical)
Analytics UI
LOCATION DATA
CMX Connect
APPLICATION DATA
Mobile Application Server
Depending on Application Layer
HyperLocation
Access Points
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MVP 1 -- Counting
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Scalable video analytics in crowded scenes
Phase 2
• General DL pipeline based on nvidia-docker
overlay networking and swarm with support
for thousands of nodes and 10’s of
thousands of containers
• High performance video pipeline based on
gstreamer
Phase 1
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• Tracking involves extracting the spatio-temporal history of each person in a scene solving the assignment problem using Jonker-Volgenant algorithm
• We use a Convolutional Network (CNN) + LSTM
• Tracking estimation is performed by multiple Kalman filters assigned to each tracklet
Tracking Overview
CNN/
LSTM KF/JV
Input RGB
frames
Localization
Estimation Tracking
estimation
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Back to data fusion
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An intersection of technology and trends:
• Recent camera sensor technologies offer advanced capabilities
• Advanced cameras + analytics create powerful IoT sensors
• Visual analytics and data fusion fit naturally into the fog architecture
The industry is moving towards scalable, flexible, distributed analytics platforms
Machine Learning from End Point to Cloud
Heterogeneous Compute
Platform
CPU + GPU/FPGA
IoT Cloud Software
Advanced
Cameras
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1. Better Understanding of the public space
2. Optimization of energy management in
public buildings
CDP, MERAKI wifi, CMX, LoRaWAN, cameras, video
analytics, Energy / Asset Management
2 innovative pilots proposed by the City: outdoor/indoor
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Cisco Data Center Portfolio and NVIDIA
• Virtual workstation for high-end graphics applications
• Cisco UCS C240 M4 Rack servers
• Support NVIDIA GRID 1.0 and 2.0 with K1/K2 and M60 cards
• Support for Magma ExpressBox for higher density
• Cisco UCS B200 M4 Blade servers
• Recently introduced NVIDIA M6 MXM support on Blade server
• Cisco HyperFlex –2nd generation HyperConverged platform
• Phase 1: K1/K2 support
• Phase 2: M6/M60 support
• Deep Learning and HPC
• Cisco UCS C240 Rack servers with TESLA K80
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