s5813 - tmcreate deep intelligence in the internet of ... · preferred networks 2 preferred...

GTC S5813 - Create Deep IntelligenceTM in the Internet

of Things (IoT)

Nobuyuki Ota

Preferred Networks

2

Preferred Networks, Inc. (PFN) founded in 2014, located at Tokyo, Japan.

– Subsidiary company, PFN America is at San Mateo CA.

PFN specializes in distributed machine learning technology, with a focus

on Deep Learning, for the Internet of Things (IoT)

PFN’s goal is the realization of Distributed Deep IntelligenceTM —the

synergistic implementation and integration of Distributed Deep Learning

intelligence throughout the IoT networks

Major problems in the IoT and PFN’s approach for its resolution

Applications of Deep IntelligenceTM technologies using GPU

Distributed Deep Learning for Drug Discovery

3

Problems faced by IoT/IoE applications: Massive increase in volume, velocity, and variety of data

Massive amounts of data are generated at the edge of the network

This data is large, noisy, and has low-value density

Collecting and analyzing this data in the Cloud is not practical

4

PFN’s Solution: Online Edge-Heavy Computing and Global

analysis on Cloud Computing

5

Devices analyze data locally at the edge of the network

Edge-devices learn autonomously in real-time for superior accuracy

Machine learning models and extracted information only are sent to the

Cloud for global analysis

What is Deep IntelligenceTM?

Intelligent platform using Deep Learning through entire Networks

6

Edge-Heavy

Cloud

Sense Organize Analyze Act

Automatic & Real-Time Optimize / learn

Deep Learning

PFN’s Strategy of Deep Intelligence to IoT

1. Development of proprietary, state-of-the-art, flexible Deep Learning

method

1. Deployment in diverse edge devices and network components to

achieve Distributed Deep Intelligence

1. Integration of network and edge device control through a

comprehensive Deep Learning management system

7

Realization of Deep Intelligence in IoT/IoE with a

strong partnership with NTT, Cisco, Toyota

8

Edge Heavy Computing:

Video Intelligence Box using GPU (Tegra K1)

9

• Feature

• Advanced algorithm: deep neural nets recognize video inside box

• All-in-one: Web-cam, cpu, gpu, wifi, power and streaming service

• Battery-powered: running up to hours without external power

• An example of advanced intelligence that works on IoT devices

Retail Product Using Deep Intelligence Video Analytics:

10

• Was the ad effective? • Deep learning model

improves through

observing customer

response

Self-Learning Video Analytics

Targeted Advertising in Retail

• Video ad selected

based on recognized

features and predicted

customer behavior

• Personalized ad shown

to shopper on video

screen

• Customizable deep learning

video analytics models • Recognition of user-defined

customer features

• Prediction of shoppers’

behavior

• Feedback “closes

the loop” for

greater customer

understanding


improves through

observing customer

response




based on recognized


customer behavior


to shopper on video

screen



customer features


behavior


the loop” for

greater customer

understanding


improves through

observing customer

response




based on recognized


customer behavior


to shopper on video

screen



customer features


behavior


the loop” for

greater customer

understanding


improves through

observing customer

response




based on recognized


customer behavior


to shopper on video

screen



customer features


behavior


the loop” for

greater customer

understanding


improves through

observing customer

response


based on recognized


customer behavior


to shopper on video

screen



customer features

• Prediction of shoppers’ behavior

• Feedback “closes the loop” for

greater customer understanding

Retail Intelligence Video Analysis Prototype at ITpro EXPO 2014

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Demo included feature recognition, location detection, sending targeted ads,

security features, and real-time learning

Day two included customized feature recognition based on video feed from

day one

Dashboard snapshot illustrating visualization of the distribution

of recognized features by location on floor plan of expo site

Retail Product Using Deep Learning Video Analytics:

Product Features:

Customizable recognition of customer attributes:

– Gender

– Age

– Clothing type or color

– Any other user-specified features

Location tracking of individual customers

Targeted actions based on customer location and recognized features – Delivery of personalized ads, offers, or product information to displays or mobile

devices

Ability to “close the loop” and learn from customer response – System automatically captures customer response and uses it to update its model

in real-time for improved accuracy

Complete surveillance and security suite

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Collaborative Car to Car Intelligence:

Smart Car Networks

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• Intelligent V2X communication

• Collaborative understanding

• Model mixing and sharing

• Autonomous real-

time learning

• Integration of local knowledge

• Global analytics

• Model repository

Self driving

technology

Autonomous Real

time Learning

Multi-model

recognition

Deep Intelligence for Automobiles and Smart Cities

Self-driving car technology

– PFN began exclusive collaboration with Toyota Motor Corp in Oct. 2014 for

development of self-driving technology using Deep Learning

Dash cam analytics

– Deep Learning can add meta-information to dash cam video streams to provide

useful information for a variety of purpose, such as a safer driving.

Inter-car distributed machine learning and V2V communication

Connect Automobile to Smart City to provide integrated services

– Parking prediction

– Traffic control

– Energy control

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Medical Science:

Deep Learning application for Drug Discovery

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• Hinton’s group won the Kaggle

competition to predict Drug

Activity

• Multi-task Neural Networks for

QSAR Predictions (GE Dahl, et al

2014)

Deep Learning Application for Drug Discovery

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Chemical

compound

Assay Data Deep Neural Net • ２−３hidden layers

• 500-2500 units

• Dropout

• Minibutch SGD

PubChem

Database

100100110101000 1

0

Fingerprint

+ Activity

B

Prediction of

Drug Activity

multiple targets

(Multi-task)

1

0 Active!!

1

0 Active!!

0

1 Inactive!!

19 assays

2M substances

Multi-task improved accuracy

Distributed Deep Learning Architecture

for Drug Discovery using Parallel Distillation and GPU

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PubChem

Database

2M Substances

19 Assays

Soft target

(Dark Knowledge)

Soft target

Community

Learning

Cluster ~10 Nodes

Node

- 3GPU K40

- 54GB memory

Each Node optimizes with

Hard target + Soft target

Result: Scalability

Distributed Processing of Deep Learning using Parallel Distillation is

successfully implemented and shows better scalability

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# of Nodes

elapsed

time node * time

communicat

ion time

1 10.5719 10.5719 0.0318

2 5.2267 10.4534 0.1377

3 3.9455 11.8365 0.1284

4 2.5978 10.3912 0.1367

8 1.5417 12.3336 0.1281

# of Nodes

Ela

psed T

ime

Scalability

Result: Improved Accuracy using “Community

Learning”

Distributed Processing of Deep Learning using Parallel Distillation

shows improved accuracy

D

19

Community Learning

AUC values

0.9387

0.9413

0.9274

0.8913

0.9214

Massive Distributed Deep Learning Architecture

for Drug Discovery using Parallel Distillation and GPU

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PubChem

Database

200M Substances

1M Assays

Soft target

Soft target

Community

Learning

Cluster >100 Nodes

Node

- 3GPU K40

- 54GB memory

. .

. .

. .

. .

Practical Applications for Drug Discovery

Kinase and GPCR

– Deep Learning can predict Cross Reactivity, Side Effect, Toxicity as Multi-task.

– No structural information of target proteins is necessary

– Reduce R&D cost

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Drugs

Assays

Deep Intelligence Application for Medical Science

Predictive Model

Genome Database Chemical compound

Database Bio Assay

Database

• Integration of multiple

data type

• Community Learning

• Generalized deep learning

model solves multiple tasks

Personalized

Medicine

Drug Discovery Diagnosis

Deep Intelligence for IoT

Deep Intelligence

Edge Device Cloud

Middle

Network

• Integration of multiple

data type

• Community Learning & sharing knowledge

• Generalized deep learning

model solves multiple tasks

Healthcare Retail Automobile Smart city

• Autonomous and Real Time

• Global analysis, data center

• Entire Network is connected

as Deep Neural Net

s5813 - tmcreate deep intelligence in the internet of ... · preferred networks 2 preferred...

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