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From Technologies to Markets
© 2020
Artificial Intelligence Computing for
Automotive
Market and Technology
Report 2020
Sample
2
Glossary and definition 2
Table of contents 4
About the authors 5
Companies cited in this report 6
What we got right, What we got wrong 7
Report scope, objectives and methodology 8
Who should be interested by this report 16
Yole Group related reports 17
Three-Slide summary 20
Executive summary 24
Context 78
Market forecasts 105
o Initial statements
o Artificial Intelligence computing for automotive forecasts
o Cameras for automotive
o Computing hardware for autonomy 2019 market shares
o AI computing for automotive forecasts by segment
o AI computing for automotive forecasts by type of camera
o AI computing for automotive forecasts by type of hardware
o Key points
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
TABLE OF CONTENTS
Market trends 138
o Introduction
o ADAS vehicles
o Robotic vehicles
o Infotainment
o Conclusion
Technology trends 186
o Introduction
o Centralization
o Acceleration
o Infotainment
Ecosystem 232
o Introduction, merge and acquisitions analysis
o Autonomy
o Infotainment
Conclusion 281
o Challenge and stakes
o The value chain follows the data flow
o Summary
• Yole Développement presentation 290
3
Yohann Tschudi
As a Software & Market Analyst, Dr. Yohann Tschudi is a member of the Semiconductor & Software division at Yole Développement (Yole). Yohann
works daily with his team to identify, understand, and analyze the role of software and computing parts within any semiconductor product, from
machine code to the most advanced algorithms. Following his thesis at CERN (Geneva, Switzerland), Yohann developed dedicated software for fluid
mechanics and thermodynamic applications. Afterwards, he served for two years at the University of Miami (FL, United-States) as an AI scientist.
Yohann has a PhD in High-Energy Physics and a Master’s in Physical Sciences from Claude Bernard University (Lyon, France).
Contact: [email protected]
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
ABOUT THE AUTHORS
Biographies & contacts
Pierrick Boulay
As part of the Photonics, Sensing & Display division at Yole Développement (Yole), Pierrick Boulay works as Market and Technology Analyst in the
fields of Solid-State Lighting and Lighting Systems, where he performs technical, economic and marketing analyses. Pierrick has authored several
reports and custom analyses dedicated to topics such as general lighting, automotive lighting, lidar, IR LEDs, UV LEDs and VCSELs. Prior to Yole,
Pierrick has worked in several companies where he developed his knowledge on both general lighting and automotive lighting. In the past, he has
mostly worked in R&D departments on LED lighting applications. Pierrick holds a Master’s in Electronics (ESEO – Angers, France).
Contact: [email protected]
Pierre Cambou
Pierre Cambou has been part of the imaging industry since 1999. He initially served in several positions at Thomson TCS. which became AtmelGrenoble in 2001 and e2v Semiconductors in 2006. In 2012 Pierre founded Vence Innovation. later renamed Irlynx. to bring to market an infraredsensor technology for smart environments. He has an Engineering degree from Université de Technologie de Compiègne and a Master of Science fromVirginia Tech. Pierre also graduated with an MBA from Grenoble Ecole de Management. In 2014 he joined Yole Développement, where he is nowPrincipal analyst for Imaging activities.
Contact: [email protected]
4
Alphabet, Algolux, Amazon, AMD, Apple, ARM, Baidu, Bosch, BMW, Continental, Delphi, EasyMile, Eyesight Faurecia, Ford, Fujitsu, General Motors, Google, Infineon,
Intel, Intel MobilEye, Kalray, Lyft, Melexis, Mercedes-Benz, Microship, Microsoft, Navya, NEC, Nio, Nissan, Nuance, NVIDIA, NXP, Parrot, PSA, Qualcomm, Renesas, Samsung, Sony Softkinetic, STMicroelectronics, Tesla, Texas Instruments, Toshiba, Toyota, Uber,
Valeo, Videantis,Volkswagen, Volvo, Waymo, Xilinx, and many more
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
COMPANIES CITED IN THIS REPORT
5
1. Provide a scenario for AI within the dynamics of the autonomous automotive market, and present anunderstanding of AI’s impact on the semiconductor industry:
o Hardware for AI - revenue forecast, volume shipments forecast
o Systems - ASP forecast, revenue forecast, volume shipments forecast
o Focus on autonomous car: ADAS and robotic vehicles
2. Deliver an in-depth understanding of the ecosystem & players:
o Who are the players? What are the relationships inside this ecosystem? Who will win the “autonomous” battle?
o Who are the key suppliers to watch, and what technologies do they provide?
3. Offer key technical insights and analyses into future technology trends and challenges:
o Key technology choices
o Technology dynamics
o Emerging technologies and roadmaps
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
REPORT OBJECTIVES
6Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
WHO SHOULD BE INTERESTED IN THIS REPORT?
IC manufacturers and vendors, and IP sellers:
o Evaluate the market potential of future technologiesand products for new applicative markets
o Screen potential new suppliers for introducing newdisruptive technologies
o Monitor and benchmark your competitors’advancements
Sensor and AI-related companies:
o Spot new technologies and define diversificationstrategies
o Position your company in the ecosystem
Technology suppliers:
o Understand the strategies of both the big players andstart-ups
Equipment and materials manufacturers:
o Understand ecosystem dynamics
o Realize the differentiated value of your products andtechnologies in this market
o Identify new business opportunities and prospects
Tier 1s and OEMs:
o Analyze the benefits of using these new technologies inyour end-system
o Filter and select new suppliers
Financial and strategic investors:
o Understand the potential of technologies and markets
o Acquaint yourself with key emerging companies andstart-ups
7Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
SCOPE OF THE REPORT
Level 5
Level 4
Level 3
Level 2
Advanced Driver-Assistance
Systems
ADAS
Robotic cars
Autonomous driving
Computing close to
sensor
Centralized computing
PerformanceCloud computing
Not included in
the report
Driver environment
Infotainment
Gesture
recognition
Speech
recognition
Multimedia computing
Edge computing
Understand the impact of Artificial
Intelligence on the computing
hardware for automotive
Data center computing
8
AUTONOMOUS VEHICLES - THE DISRUPTION CASE
Two distinctive paths for autonomous vehicles
2020 should see the first commercial implementation of autonomous vehicles.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
1880 1960 2000 2020 2030 2035
Technology x market penetration
Acceleration : The speed of technology change doubles every technology shift
Improvement
of cars as we
know
5 years10 years20 years40 years80 years
Yole Développement
© August 2019
Below expectation
“cars” fulfilling needs
in a new plane of
consumption
Disruption ?
Electronics
invades cars
Electric car
maturesIndustrialization
phase
New use cases
Automated
driving
Autonomous
vehicles
Robotic cars
ADAS vehicles Robotic vehicles
9
AUTONOMOUS VEHICLES - THE ROBOTIC DISRUPTION CASE
Two distinctive paths for autonomous vehicles
Levels became marketing definitions, but they do not represent the reality.
The reality is whether it is autonomous, or whether it is not.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
Where?
Anywhere
Designated
places
Designated
areas
Limited
distance
Taxi
Autonomous
driving
How?
Historical
players
New
entrants
ADAS vehicle
Anywhere
Low
speed
Any
speed
Tech giants
Startups
?Medium
speed
Shuttle/bus
Robotic vehicle
Personal car Robotic Mobility-as-a-Service
Level 4-5Level 3?
Is L3 relevant?
Does it exist?
Level 1-2 Level 2+ Level 2++
2015 2022
2015
2020
2015
2020
10
CAMERAS FOR ADAS VEHICLES
Device and technology segmentation
Eight different automotive applications use cameras.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
In-cabin
ADAS
Viewing
Dash/blackbox
Gesture recognition
Forward ADAS
Night vision
Mirror replacement
360° surround
Rearview/backup
Driver monitoring
Camera
3D camera
Camera
Thermal camera
Camera
Camera
Camera
Camera
Yes
Yes
Yes
Yes
No
No
Yes
Yes
AI before 2025?Type of cameraUse case
11
GOAL OF CAMERA
Why use AI ?
Using AI for different applications for each camera.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
In-cabin
ADAS
Viewing
Dash/blackbox
Gesture recognition
Forward ADAS
Night vision
Mirror replacement
360° surround
Rearview/backup
Driver monitoring
Recording
Change radio, handle
volume of music…
Autonomous driving
Bad weather/night conditions
Replace mirrors
Understand the
environment around the car
Autonomous driving
Monitor driver’s behavior /
verify looking at the road
Pedestrian detection
Gesture
recognition
Driver behavior such
as dizziness
Pedestrian recognition, traffic
light recognition, lane
recognition, object recognition
No available technology, still
in R&D
No need for AI yet
3D reconstruction,
orientation of cars, object
recognition
Pedestrian recognition, object
recognition, auto parking
Why use AI?Goal of cameraUse case
12
FOUR BUSINESS MODELS
Each business model gains value from different sources
Most of the net value will come from mobility as a service.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
Four main business models can beidentified:
- Car manufacturer : OEMs.
- Autonomous driving : Softwareand hardware: they are developingthe brains of cars and all thedriverless applications.
- Car electrification : Battery andpower train manufacturers:- theyare turning cars into electricpowered systems.
- Mobility as a service : Serviceproviders (robotaxis or shuttles):-they will offer transportation as aservice
Car manufacturer
Mobility as a service
Car electrification
Autonomous driving
But the major value flow will go
to the service provider: by
changing consumer use they will
overtake the others
13Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
FROM IMAGE PROCESSING TO FUSION PLATFORM
Frame processing +
other sensors
Fusion platform
Vision processor from
MobilEye
Frame processing
Vision processor
• Amount of data processed
• Performance
• Consumption
AI algorithms
Price
per unit
> $1000
$10
< $1Set of pixels processing
Image Signal Processor
Image processing
Standalone ISP from Altek
Fusion platform from NVIDIA
Algorithm
complexity
$100Sensing Processing Unit – ISP stacked
with CIS
Acceleration
Centralization
Computer vision
14
FROM LEVEL 0 TO LEVEL 2++ ON ONE SIDE, ROBOTIC ON THE OTHER
Inclusion of accelerators and multiplication of the number of chips
Computing introduces AI and follows what has been seen in consumer.
• Level 0 to Level 2+/2++ are differentiating mostly by improved functionalities such asAutomatic Emergency Braking (AEB) and some new functionalities such as Traffic Jam Assist(TJA) or Lane Keeping Assist (LKA)
• On the robotic side, full autonomy was first realized in closed area at low speed(<15miles/h) to open designated area and at medium speed (<30 miles/h)
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
Implementation of AI : following what have been done in
consumer applications
Implementation in SoC or as a standalone
chip of accelerators
Multiplication of the number of
computing chips
These improvements are realized thanks to the introduction of AI
algorithms and its related hardware
x2 x4 x8
15
TOWARDS ACCELERATORS IN AUTOMOTIVE
Focus on the Intel Mobileye evolution
Introduction of a programmable DL algorithms accelerator.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
7nm
TSMC
24 TOPS @ 10W
In production 2021
Accelerator
embedded in SoC
28nm
ST
2.5 TOPS @ 6W
In production 2018
Vision Processor Units
embedded in SoC
Intel keeps its strong technology based on computer
vision and running on vision processors units but open
its black box to deep learning with the introduction of
an accelerator
Level 2+
Level 2++
16
TOWARD ACCELERATORS IN AUTOMOTIVE
Focus on the evolution of NVIDIA products
NVIDIA multiplies the number of GPUs to gain in performance after each new GPU created.
Introduction of an accelerator in the Xavier SoC.
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
Product Name NVIDIA Drive PX NVIDIA Drive PX 2 NVIDIA Drive Xavier NVIDIA Drive Pegasus NVIDIA Drive Orin
Introduction 2015 2016 2017 2017 2019
SoC Name Tegra X1 Parker Xavier Xavier Orin
Process Technology 20nm SoC 16nm FinFET 12nm FinFET 12nm FinFET 7nm FinFET
SoC Transistors 2 Billion (Tegra X1) N/A 7 Billion (Xavier) 7 Billion (Xavier) 17 Billion (Orin)
Accelerator - -1x DLA
1x PVA
2x DLA
2x PVA
2x DLA
2x PVA
Total Chips 2 x Tegra X12 x Tegra X2
2 x Pascal MXM GPUs1 x Volta
2 x Volta
2 x Turing2 x Ampere
Compute N/A 20 DLTOPs 30 TOPs 320 TOPs 400 TOPs
TDP 20W 80W 30W 500W 130W
Accelerator
embedded in SoC
Vision Processor Unit and
Image Signal Processor
embedded in SoC
NVIDIA Xavier SoC
announced for Level 2++In production 2021
PVA: Programmable Vision Accelerator
DLA: Deep Learning Accelerator
L2++
17
COMPUTING HARDWARE FOR AUTONOMOUS DRIVING
Ambarella CV2
Ambarella CV22
Hailo-8 DL
Intel MobilEye EyeQ3
Intel MobilEye EyeQ4
Intel MobilEye …
Kalray Coolidge
NVIDIA
Drive PX 2
NVIDIA
Drive PX Xavier
NVIDIA Drive PX Pegasus
NVIDIA Drive Orin
NVIDIA Drive Orin x2
NXP S32V234
Qualcomm Snapdragon Ride
Qualcomm Snapdragon Ride
Accelerators x2
Renesas R-Car H3
Tesla FSD
TI Jacinto TDA3
Toshiba
Visconti 4Xilinx Zynq
Ultrascale+ EV
1
10
100
1000
0,1 1 10 100 1000Log scale
Performance (TOPS)
Log scale
Power dissipation (W)
Level 1-2
Level 2+
Level 3?
Level 2++
Robotic vehicles
are using chips in
>100W range
ADAS computing
is using chips in
the 2W to 20W
range
1Peta
flop
Next battleground
for the ADAS industry
SiP
The use of accelerators
in SoCs or as
coprocessors enables
increased performance
faster than consumption
Level 4-5
5 years 5 years 5 years
~100Tops/W~10Tops/W~1Tops/W~0.1Tops/W
Robotic
ADAS computing race :
higher performance for
minimum consumption
2020 2025
Vision processor Accelerator Multiple chips
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
18
FROM SENSOR SUITE TO COMPUTING SUITE FOR AUTONOMY
Level 1
ACC
Level 2
PALKA TJA
Level 2+/2++
AEB L2+
DM
Level 3/4
HP
Level 5/Robotic
AP
Levels and
functionalities AEB L1 AEB L2
VP
MCU FPGA
Centralized platform
High
Performance
and ASP
Radar
Forward/Rear Cam.
Surround Cam.
Lidar
Fusion
Fusion Fusion
Technology penetration
Increasing number of neural networksDeep Learning algorithms
2016 by Tesla – 2021 for others 2030 20402012
Computer Vision algorithms
1 5 >10
Low
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
19
FOUR TYPES OF PLAYER
LEVEL OF INVESTMENT
& AUTONOMY
ADDED VALUE
COMPUTING HARDWARE PLAYERS
Provide the silicon and the software stacks to
OEMs
Develop their own autonomy stack
by using the silicon provided
Use the full solution provided by
computing hardware company
Develop full solution by themselves
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
20
THE VALUE CHAIN FOLLOWS THE DATA FLOW
SenseSensor $0.1 - $1
ProcessHardware $1-$10
Skiing
99%
ComputeHardware $10-$100
IPLicense/Royalties
AnalyzeHardware >$1000
The output of the
process step is of the
same type as the input.
Processing value is
measured through how
the compute step is
facilitated
In addition to image/sound,
information is provided. The
quality and precision of this
information as a function of
the computing power defines
the value of the compute
step
Maximum level of value is reached here, at the analyze step,
with dedicated information that is used for understanding
habits, interests,… for targeted ads
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
21Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
FORECASTS
22
M&A ANALYSIS AND ECOSYSTEM
Artificial Intelligence Computing for Automotive 2020 | Sample | www.yole.fr | ©2020
23
Contact our
Sales Team
for more
information
Sensors for robotic mobility 2020
Sensing & Computing for ADAS 2020
Imaging for Automotive 2019
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YOLE GROUP OF COMPANIES RELATED REPORTS
Yole Développement
24
Contact our
Sales Team
for more
information
Nvidia Tegra K1 Visual Computing Module
Triple Forward Camera from Tesla Model 3
Automotive Teardown Tracks
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YOLE GROUP OF COMPANIES RELATED REPORTS
System Plus Consulting
25
CONTACT INFORMATION
o CONSULTING AND SPECIFIC ANALYSIS, REPORT BUSINESS
• North America:
• Steve LaFerriere, Senior Sales Director for Western US & Canada
Email: [email protected] – + 1 310 600-8267
• Chris Youman, Senior Sales Director for Eastern US & Canada
Email: [email protected] – +1 919 607 9839
• Japan & Rest of Asia:
• Takashi Onozawa, General Manager, Asia Business Development
(India & ROA)
Email: [email protected] - +81 34405-9204
• Miho Ohtake, Account Manager (Japan)
Email: [email protected] - +81 3 4405 9204
• Itsuyo Oshiba, Account Manager (Japan & Singapore)
Email: [email protected] - +81-80-3577-3042
• Toru Hosaka, Business Development Manager (Japan)
Email: [email protected] - +81 90 1775 3866
• Korea: Peter Ok, Business Development Director
Email: [email protected] - +82 10 4089 0233
• Greater China: Mavis Wang, Director of Greater China Business
Development
Email: [email protected] - +886 979 336 809 / +86 136 61566824
• Europe & RoW: Lizzie Levenez, EMEA Business Development Manager
Email: [email protected] - +49 15 123 544 182
o FINANCIAL SERVICES (in partnership with Woodside Capital
Partners)
• Jean-Christophe Eloy, CEO & President
Email: [email protected] - +33 4 72 83 01 80
• Ivan Donaldson, VP of Financial Market Development
Email: [email protected] - +1 208 850 3914
o CUSTOM PROJECT SERVICES
• Jérome Azémar, Technical Project Development Director
Email: [email protected] - +33 6 27 68 69 33
o GENERAL
• Camille Veyrier, Director, Marketing & Communication
Email: [email protected] - +33 472 83 01 01
• Sandrine Leroy, Director, Public Relations
Email: [email protected] - +33 4 72 83 01 89
• Email: [email protected] - +33 4 72 83 01 80
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