Deep Learning
The Driving Engine for The Internet of Things

Toru Nishikawa
President & CEO

Preferred Networks, Inc.

Internet of Things

Deep Learning

Our Strategic Partners

Deep Learning

Internet of Things

The National Cancer Center in Japan and Preferred Networks startcollaborative research in deep learningAccuracy for Breast Cancer Diagnosis

Mammography80%

1 in 5 womenAccuracy for Breast Cancer Diagnosis

Mammography

80%

SOTA Liquid Biopsy90%

1 in 10 womenAccuracy for Breast Cancer Diagnosis

Mammography

80%

SOTA Liquid Biopsy

90%

SOTA Liquid Biopsy
with Deep Learning99%Less than 1 in 100 womenPaintsChainer (#PaintsChainer)

http://free-illustrations.gatag.net/2014/01/10/220000.html

Anomaly prediction for FANUC robots
Sensor data obtained fromfanuc robot‘s reducers

NormalExtract anomaly
signals using deeplearning technologies

Anomaly

Detectanomaly signals

Deep learning
based methods

Existing methods

Detect just before
the failure
Robotfailure

異常スコア

Detect 40 days
before the failure

ThresholdRobot
15日前 failure

Elapsed time

Can predict the failure much earlier thanthe existing methods

Deep reinforcement learning makes autonomous driving more robustDeep reinforcement learning is also effective in controlling unstable mobiledevices, such as drones

Amazon Pick Test Results

Place12345678TeamDelftPFN (Preferred Networks)
NimbRo Picking
MITTeam K
ACRV
CMU-HARP
C^2M

Score
105
105
97
67
49
423321

We are accelerating deep learning application research and development byproviding open source software

Released in2015

The volume of data generated from IoT devices is huge…

200 EB2015

…and increasing rapidly

1.6 ZB

200 EB20152020

To process this huge amount of data, we need to apply parallel computing
to deep learningPerformance Comparison in Scalability of Deep Learning Frameworks

Available early 2017

Scaling Result for CNTK, MXNet, TensorFlow and Chainer

Decentralized computing architecture is one possible solution to eliminate
the network bottleneck
Cloud Computing

Edge Heavy Computing

Summary of DIMo (Deep Intelligence in-Motion)


DIMo is a platform for Deep Learning R&D and product development
Edge/Fog sideOn-premise

Cloud side

Edge/Fog Device

DIMo Development
Appliance forPrivate Data Center(by early 2018)

DIMo Cloud PortalController

DIMo Agent
Edge/Fog Device
DIMo Agent••••••Data/Model repository
Toolkit for R&D
Data Annotator
Application plugins
Edge App Monitoring
etc

DIMo Development
Cluster…

DL application
deployment andmanagement foredge and fog

Training data management

Packages for applications–Computer vision–Anomaly detection26

Development Env foron-premise and Cloud

On-premise version
can privately utilize
most toolkit provided
on the Cloud.

DIMo Software Stack for Application Development
User applications
Industries (PFN-involved)

Industries (Partners)
Healthcare

RetailSurveillance

Self-driving/ADASOptimization

DrugdiscoveryCRM

Security

Connected

Predictive
maintenance

iPS cellAd
optimization

Tracking

Reinforcement
learning

Detect/Track/Rec
ognizeDistributed/Curriculum

SensorfusionTime-series
RNN /Representation

Multi-modal

Camera UI
KanohiAnnotation

Chainer

Machinelearning

Hawk

Feedback/Action

Statistics

SensorBee™: Intelligent Streaming ETL for IoT
27…
ManagementTools

Computervision

Deep Intelligencein-Motion (DIMo)

Manufacturing

Libraries

AutoWe will launch the world’s first edge-heavy deep learning platform formanufacturing✓ Deep learning for industrial IoT✓ Edge heavy computing✓ Orchestrating various types ofdevices in real time

Conclusion

Deep Learning

Internet of Things

WE ARE HIRING!
info@preferred.jp

Copyright © 2014Preferred Networks All Right Reserved.