Fiscal Year Ended March 31, 2017

Results Briefing
Digital Media Professionals Inc.
May 25, 2017

The views and forecasts that appear in these materials represent determinations made by the Company at the time the materials
were created. The accuracy of the information therein is not guaranteed.
Copyright (C) Digital Media Professionals Inc. All Rights Reserved.
Please be aware of the possibility that actual performance and results may differ considerably due to a variety of factors.

11

Explanation of Results,
Fiscal Year Ended March 31, 2017

2

Fiscal Year Ending March 2018,
Business Forecast

3

Proposition for Future GrowthCopyright (C) Digital Media Professionals Inc. All Rights Reserved.

2

1Explanation of Results,
Fiscal Year Ended March 31, 2017

2

Fiscal Year Ending March 2018,
Business Forecast

3

Proposition for Future Growth

Copyright (C) Digital Media Professionals Inc. All Rights Reserved.

3

Operating Environment of the Company
Semiconductor Industry
 Rising demand for infrastructure-oriented chips such as chips for servers and datacenters, etc., driven by IoT and AI market growth Favorable demand for memory and on-board automotive equipment applications
 Demise of Moore's law and rapid advance of AI technology
 Continued large-scale M&A centered on automobiles and AI/IoT fields(Intel/MobileEye, Softbank/ARM, Qualcomm/NXP, and more)

AI / Visual Computing Field Rapid growth in GPU applications for cloud-based artificial intelligence processing Self-driving cars and AI related devices (e.g., Amazon Echo) moving into focus
 Rapid growth in demand for large-volume data processing power for edge computing
driven by advances in IoT/AI
 Development competition unfolding in low-power consumption, low-cost AI processors

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4

FY Ended March 31, 2017

Results Highlights
Sales Revenue recognition of licensing income from existing customers and licensing
income from Deep Learning-type video/image recognition engine "ZIA" Classifier (*)
 Sales of image processing semiconductor "VF2" miss expectations

Earnings(*) Details are available on page 23

 Research and development costs continuing from the previous fiscal year for thedevelopment of next-generation image processor "RS1" (*)
 Recognition of a ¥13 million special gain from final liquidation proceeds in connection
with the divestiture of CogniVue Corporation after the sale of shares in the previous
fiscal year Recognition of a ¥106 million special loss from impairment loss recognition on noncurrent assets related to image processing semiconductor "VF2“(*) Details are available on page 16
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5

FY Ended March 31, 2017

Results Summary (P/L)(Unit: million yen)

FY ended
March 31,2016

FY ended
March 31,2017

YoY change

Net sales

733

694

-39

Operating loss

-176-263-87

Ordinary loss

-193-262-69

Current net loss

-64-365-301(Amount)

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6

FY Ended March 31, 2017

Results Summary (B/S)(Unit: million yen)

End of March2016

End of March2017

IncreasedecreaseamountCurrent assets

1,984

1,668-316

Non-current assets

260

112-148

2,244

1,780-464

Current liabilities

226

91-135

Non-current liabilities

18180

Total liabilities

245

110

-135

Total net assets

1,999

1,670-329

Total liabilities andnet assets

2,244

1,780-464

Total assets

 Equity ratio of 93.6% remains at a high level
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7

FY Ended March 31, 2017

Results Summary (Efforts in the period under review)

IP Core License Field

Initiation of sales and marketing efforts for the new 3D graphics
IP core "M3000" series
Realized small size combined with highperformance
Revenue recognition from sales of middleware library "IPSL"
co-developed with Toyota Tsusho Electronics Corporation(now NEXTY Electronics Corporation)
High-speed processing enabled by general-purpose
microcomputer GPU
Contributing to cost reductions and ease ofcustomization

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8FY Ended March 31, 2017

Results Summary (Efforts in the period under review)

SoC / Module Business Field

Roll-out of sales and marketing activities for amusement-applicationgraphics semiconductor "VF2"
Performance missed plan due to effects from decreased demandamid changing regulatory trends in the amusement industry and due
to customers’ delay in choosing new models

Operating tie-up with BANDAI NAMCO Entertainment Inc.
(announced on January 31, 2017)

×Co-development of next-generation LSI "RS1" as "VF2" successor
model for next-generation platform "HAYABUSA" for game machine
applications
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9FY Ended March 31, 2017

Results Summary (Efforts in the period under review)

Professional Services Field

Received commission for “Crosscutting Technology
Development Project to Promote IoT" of NEDO (*)
Promoted development of "AI platform based on a powersaving AI engine and a cloud integrating dissimilar
engines"(*) Details are available on page 20

Received first commission for "ZIA" Classifier after announcement asan R&D result
Efficient assessment of image data and image content
Improvement in the accuracy of image
discrimination using Deep Learning technology

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1Explanation of Results,
Fiscal Year Ended March 31, 2017

2

Fiscal Year Ending March 2018,
Business Forecast

3

Proposition for Future Growth

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11

FY Ending March 31, 2018

Business Forecast
 Expectations for 58.4% year-on-year growth in sales revenue due to initiation of
volume production and shipments of next-generation image processing
semiconductor "RS1" and expansion of professional services
1,100
Net sales (Unit: million yen)
(Estimate)
1,000

733

750

694

464

500
355
250

0
FYFYended
March31, 2015
ended March 31, 2014

FYFYFY
ended March 31, 2016 ended March 31, 2017 ending March 31, 2018

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12

FY Ending March 31, 2018

Business Forecast(Unit: million yen)

FY ended
March 31,2017(Actual)FY endingMarch 31,2018(Forecast)

Net sales

694

1.100

406

58.4%

Operating loss

-263-90173-

Ordinary loss

-262-90

172-

Current net loss

-365-90275-Sales

YoY change(Amount)(Increase-decreaserate)

Earnings

 Expectation for sales of next-generation LSI"RS1"
 Increase in IP licensing including "ZIA"
platforms

"RS1" development cost reduced
compared with the previous fiscal year
Expectations for costs associated with thevolume production of "RS1"

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13

1Explanation of Results,
Fiscal Year Ended March 31, 2017

2

Fiscal Year Ending March 2018,
Business Forecast

3

Proposition for Future Growth

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14

FY ending March 31, 2018

Measures in the time ahead

Strengthening ofexisting businesses+Promotion of businessin the AI field Next-generation image processing LSI "RS1" to be readied for manufacture
and volume production

 Enhancement of the "ZIA" platform product line-up Promotion of development commissions from NEDO for power-saving AI engines
 Promotion of AI related professional services

Diversification of earnings sources
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15

About "RS1"
Next-generation image processor LSI for next-generation entertainment platforms
Real time 3Dperformance Successor to game machine application graphics
LSI "VF2"

Hugely superior video/3D performancecompared with competitor products Co-development with BANDAI NAMCO
 Ongoing shipments of sample products (since May2017)

Competitor CCompetitor A Under assessment by key customers

Competitor A - Nextgeneration (Projection)
Competitor B

 Volume production scheduled to start this fiscal year

2D video
performance

Characteristics of the "RS1"Specifications optimized for amusement applications•Delivers high performance and low power consumption for both 3D and 2D graphics Application viable for all pachinko
parlor slot machines and commercial game machines, etc.

Gained significant performance•

Delivers 3D graphics processing performance operating at up to six times the speed of the "VF2" at the same low power
consumption as the "VF2"•2D graphics powered by 16 high-performance video machine units operating at up to three times the speed of the "VF2"
Delivers high resolution and smooth image representation.

Enhanced added value through modulization

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16Regarding AI Markets
AI markets to grow to ¥86 trillion in market size by 2030
Diffusing from IT application fields such as e-commerce, AI passed into everyday
environments through the interconnection with IoT and is destined to see outsizedmarket growth and development

Market size of AI
related industries¥3trillion

2015

¥86
trillion¥23
trillion

2020

2030

Source: Ernst & Young Institute Co., Ltd., "Creation and Destruction Brought On By AI" (2015)
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Artificial Intelligence Technology Destined toBecome Infrastructure in Every Industry

The environment surrounding artificial intelligence (AI)Manufacture(Industrie4.0)

Mobility

Medical ITEnergy/InfrastructureHome andFamily
IT Infrastructure

Financialservices

Crime
prevention/
Surveillance

Agriculture,
forestry, andfisheries

Managementadministration

Restaurants/Retailing

Construction

Successful application in awide range of fields
Cloud-based
processing
concentrated
operationstype AI

Robots+Big Data

AI

Machine learning: Technique for extracting certain types of patterns called
models based on Big Data analysis
Data gathering
Reflection of learning

Edge-based
distributed
processingtype AI

IoT (M2M)

Edge AI
Automobile

Data gathering
Reflection of learning

Edge AI
Medicalequipment

Data gathering
Data gathering

Data gathering

Reflection of learning

Reflection of learning

Edge AI

Edge AI

Surveillancecameras

Industrialmachinery

Data gathering
Reflection of learning
Reflection of learning

Edge AI
Smartphones

HomeAIEdge
electricappliance

Source: SMBC Nikko Securities Inc.; Development Trends in Artificial Intelligence (AI) and their Impact

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About Edge AI
 Many sensors are to be transposed to cameras
 Number of network cameras to be shipped in 2020: up to 350 million units (*)
 Explosive growth in data volumes (700PB/day) Applications posing real-time requirements (e.g., car driving, AR/VR) Privacy concerns(*) Source: Intel Corporation

Edge processing + Real time processing + Security = Business chances (existing and new markets)

Comparison of data volumes
Edge AI processor
integrated cameras

20 MB

Burglar intruding

10 byteCopyright (C) Digital Media Professionals Inc. All Rights Reserved.

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NEDO
(Crosscutting Technology Development Project to Promote IoT)

Started development of artificial intelligence platform based on
power saving AI engine and cloud able to integrate dissimilar
enginesAmount awarded DMP under the contract (total amount): ¥475 million
Duration of the commission: June 2016 - March 2019

Project outline• Development of a power saving AI engine whichenables AI algorithms to process 10 times more efficientthan predecessors did• Development of an AI platform for integrating
dissimilar engines on the cloud side

Accelerate DMP AI Business
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20

Structure of the NEDO Project

Crosscutting Technology Development Project to Promote IoT(Fiscal year 2016 to fiscal year 2020)
Development of AI platform based on a power saving AI engine and a cloud integrating dissimilar enginesCommission

Development of an edge-based
AI platformaDevelopment of a cloud-based AI platform
National Institute of Advanced Industrial
Science and Technology (AIST)

Development of lowenergy consumption
AI processor

Development of design
and mounting tools

Subcontracting

Packagemounting
technology

System software for the cloud
integrating dissimilar engines

Architecture for theintegration of dissimilar
engines

Advisory companies

and more

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21

About "ZIA" platforms
Deep Learning / AI / ZIA™

 Based on the technologies accumulated since the founding as Japan's only GPU vendor,
the AI platforms of DMP form novel software/hardware products fusing AI/Deep Learningknowledge

Software ProductOn saleStage 1Image classificationengine

New saleslaunchStage 2AI processors for edge
computingHardware ProductUnderdevelopment

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ZIA Classifier
Stage 1 "ZIA" series image recognition and classification engines using
Deep Learning Categorization by item out of large-volume image data as required by customers, withefficiency-enhanced Deep Learning performance and with processing time shortened Ongoing co-development of applications for the creation of new services using "ZIA" ™
together with large service-related Japanese corporations

Take a car

Classify

StopSelfStudy

WalkImprove
Accuracy

Open anumbrella

Input

Score

DMP ImageClassification
EngineWalking underan umbrella

Video Data

Deep Learning

DMP Unique
Algorithm

Enhanced accuracy through DeepLearning. Delivers human-likeflexibility of judgment in assessing
the actions of individuals
surrounded by a crowd.

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ZIA DV700
Processor with ultra-low power consumption
specialized for Deep Learning inference processing
applicable to all data categories

 Stage 2 product of the "ZIA" product platform using AI technology of DMP
 Processor with ultra-low power consumption suited for edge-computing AIprocessing, capable of inference processing applicable to all data
categories including images, videos, and voice data, etc.
 Combines low power consumption and high performance with flexible
response capability regarding functions and performances required for adiversity of applications including security cameras

Size

Performance (FPS)

Small size

Highperformance

2.3 mm 2TSMC28HPC+(Operating frequency750 MHz)

GoogLeNet processing:
116 FPSDMPDSP A
ZIATM DV700

DSP B

DSP CPower (W)Powerconservation
GoogLeNet processing:
670 mW

GPUDMPofZIATM DV700 Company A

DSPofCompany B

DSPofCompany C

DSPofComp
any C

DMP
ZIATM DV700

GPUofCompany A

DSPofCompany B

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DSPofCompany C

DSPofCompany D

24Proposition for Future Growth
Added
value

Entry into the next added value and growth market
Expansion into thegrowing IoT field

Expansion into the AI /Deep Learning* fields

Products achieving advanced image
processing such as wearables,
automotive applications, residential
applications, industrial application,and consumer electronics, etc.

Active use of technologies
accumulated by the Company
Entry into the AI / Deep Learningmarkets

Development of software andhardware products for AI applications
centered on the ZIA platform

Establishment of a SoC business platform inthe amusement market where we can win
Continuing from the "VF2", steady progress in new
product development for the "RS1"
Impending market entry and volume production start
Markets

Phase 1

Phase 2

※ A type of machine learning application for therecognition of diverse types of data andpatterns such as image and voice recognition
using multiple layers of neural networks.

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