How To Accelerate
The Journey To
Autonomous Vehicles
Flexibility, robustness and standard choices
are key to the transformation

How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Why you should read thisAutonomous workloads are changing the way wedesign products such as autonomous and semiautonomous vehicles. It’s an enormous opportunity
to scale design with more flexible, cost-effective
methods to deliver products that are more reliable,robust and safe. But there are roadblocks along
the journey. Here are the drivers, challenges andpotential solutions to enable this vision of the future.

Introduction

Autonomous workloads promise to deliver devices and systems
that can safely decide and act for themselves to enable operational
efficiencies working alongside humans or taking humans out of theloop, opening up valuable time that can be spent elsewhere
What vertical segments are most keen
to embrace autonomous workloads?
Automotive and industrial. In the case ofautomotive, it’s shaping up as a win-win
embrace: Not only are designers pushingincreasing amounts of autonomous
functionality in their vehicles, but theirfactory lines and supply chain stand togain just as much by leveraging
utonomous workloads.

The design landscape is fragmented at
the moment and requires a new way of
thinking about how to design for these
new workloads. Autonomous workloads
will need the fundamental building blocks
including heterogeneous compute, safety,
security and machine learning, that arecommon across autonomous-based
use cases.

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How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Section 1
Why automotive companies look
to autonomous workloads

The need for autonomous
decision-making workloads
that are not dependent
upon human interaction isclear, both for safety andefficiency reasons.

1.5
Seconds

Average time a driver takes
to react and hit the brakes

0.5
Seconds

A car equipped with visionsystems, radar and LIDAR

Various sensors tied into a vehicle’s braking system can react far more quickly thanhuman reflex. It takes the average driver 1.5 seconds to react and hit the brakes
when a child runs into the street; it takes a car equipped with vision systems, radarand LIDAR just 0.5 seconds. The combination of intelligent hardware coupled withthe software running atop it – which does not rely on a connection to the cloud – will
enable this not only in driverless vehicles but in the factories they emerge from.
Automotive factory managers want to leverage autonomous workloads to boost
manufacturing productivity, improve KPIs and improve operational safety. They
want to make their factories more flexible (and therefore more profitable) in building
customized products with efficiency and adapting their product mix on the fly.
Autonomy will lead to increasingly more efficient and intelligent robotic machinestied into ever more sophisticated building and networking systems to deliver gains
not only in manufacturing but in the larger supply chain and logistics operations.
Managers want to transform their business by integrating their factories more tightly
with the wider supply chain and logistics providers. Executives want to build fewer
but more flexible factories to help achieve certain environmental objectives. Here,the goal is one autonomous chain from ordering, to raw material requisitions, to lastmile delivery, taking humans out of the loop or creating a safer and more effective
human-machine collaboration.
Further enhancing the automation capabilities of ADAS (advanced driver-assistancesystems) and IVI (in-vehicle infotainment), technology will make our current vehicles
safer, while new technology advacements powering driverless systems will address
the growing demand for last-mile delivery, enable fixed-route shuttles, and transformroad-freight and ride-hailing services.
This is a tipping point for many companies in the history of compute. In automotive,
already companies such as Waymo, Volkswagen, General Motors and Mercedes
are taking advantage of autonomous workloads and new compute approaches.
Competitors need to move quickly to reduce this first-mover advantage in a market
that has tremendous long-term potential.

02How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Section 2Challenges to deploying the new workloads

There are layers uponlayers of complexitythat must be addressedin designing forautonomous workloads.
Distributed sensors need to be unified
to offer better insight and support
the computational burden. Disparate
systems and a dizzying range of softwareand hardware choices can make chartinga path to autonomy challenging. For
example, many current solutions forautomated driving are based on bulky,
high-power and costly standalone
GPU chips. Others require proprietary
solutions, which severely constrains
design flexibility. However, to make itsuitable for widespread commercial
use, Tier1 and OEMs are looking for amore power-efficient and cost-effective
solution suitable for mass production.
To embrace the vision, we need tounderstand how fundamentally different
autonomous workloads are fromtraditional workloads and confront thechallenges that complexity presents.
Autonomous workloads are definedby receiving input and generating an
output in their simplest terms – theability to sense and take intelligent
action. Thanks to the incorporation of AI
and machine learning, the magnitude ofworkload is far greater when comparedto something like the narrow scope ofoperation surrounding, for example,
contemporary ADAS. This requires afundamental reconsideration of design,embracing emerging technologies
and methodologies.
Autonomous is also more complex than
traditional workloads because of theoverlapping and interconnected natureof systems in industrial applications.
There may be a machine vision systemwith its own set of data and algorithms
that needs to integrate with robot
vehicles that are alerted by the visionsystem that a task has been completed
and pickup is required. Integration may
also be required for other points along
the supply chain. For example, vehicles
rolling down the manufacturing line will
benefit from just-in-time parts arrivals

thanks to the more integrated, near realtime connection with the supply chain.
In both automotive design and industrial
machines, there are the enormous
computational requirements aroundcapturing, fusing, analyzing and acting
on enormous (and different – cameras
and computer vision to LIDAR to GPSinputs) amounts of data. Layers ofsensor inputs must be processed in realtime in order to predict situations thatmachines can anticipate and react to.
The computational power and efficiency
to run the algorithms that process thedata is what makes the difference.
Configuring these systems is costly.
Compute alone can cost thousands ofdollars, while the sensors themselves
sometimes cost tens of thousands ofdollars. Safety demands redundancy
across the range of LIDAR cameras,
radars, and that raises costs. As wemove further away from driver control
toward L4 and L5 autonomy, sensor andcomputational costs ramp significantly.

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How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Section 3
How to scale hardware
There are layers uponlayers of complexitythat must be addressedin designing forautonomous workloads.
Distributed sensors need to be unified
to offer better insight and support thecomputational burden. Disparate systems
and a dizzying range of software andhardware choices can make chartinga path to autonomy challenging. For
example, many current solutions forautomated driving are based on bulky,
high-power and costly standalone
GPU chips. Others require proprietary
solutions, which severely constrains
design flexibility. However, to make itsuitable for widespread commercial
use, Tier1 and OEMs are looking for amore power-efficient and cost-effective
solution suitable for mass production.

Autonomous is also more complex than
traditional workloads because of theoverlapping and interconnected nature ofsystems in industrial applications. Theremay be a machine vision system with
its own set of data and algorithms thatneeds to integrate with robot vehicles
that are alerted by the vision systemthat a task has been completed andpickup is required. Integration may also
be required for other points along thesupply chain. For example, vehicles rolling
down the manufacturing line will benefit
from just-in-time parts arrivals thanks
to the more integrated, near real-timeconnection with the supply chain.
In both automotive design and industrial
machines, there are the enormous
computational requirements aroundcapturing, fusing, analyzing and acting
on enormous (and different – cameras
and computer vision to LIDAR to GPSinputs) amounts of data. Layers ofsensor inputs must be processed in realtime in order to predict situations thatmachines can anticipate and react to. The
computational power and efficiency torun the algorithms that process the datais what makes the difference.
Configuring these systems is costly.
Compute alone can cost thousands ofdollars, while the sensors themselves
sometimes cost tens of thousands ofdollars. Safety demands redundancy
across the range of LIDAR cameras,
radars, and that raises costs. As wemove further away from driver control
toward L4 and L5 autonomy, sensor andcomputational costs ramp significantly.

To embrace the vision, we need tounderstand how fundamentally different
autonomous workloads are fromtraditional workloads and confront thechallenges that complexity presents.
Autonomous workloads are defined byreceiving input and generating an output
in their simplest terms – the ability tosense and take intelligent action. Thanks
to the incorporation of AI and machinelearning, the magnitude of workload is
far greater when compared to something
like the narrow scope of operationsurrounding, for example, contemporaryADAS. This requires a fundamental
reconsideration of design, embracing
emerging technologies
and methodologies.
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How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Section 4
How to think about software
For autonomous systems
to succeed, an entire
software stack andsoftware ecosystemneed to be developed.

that abstracts many of the details about how the underlying systems work fromthe people who are thinking about the application level.
Traditional companies offer “sticky” proprietary software with no room for thedeveloper to put his or her own programs on. Newer entrants have given thesestalwarts a wakeup call with open systems, enabling the developer to have access toit or at least some portion of the control system to put their own control. Yet, autonomy
needs to balance openness without losing all “stickiness” in order to head off thedisruptors who propose totally open systems.
Customers that have much more demanding safety requirements will requiresafety-certified RTOS and middleware solutions. All of these elements will havesome very demanding requirements as far as being able to achieve safety
certifications such as ISO 26262.
As autonomous vehicles move from prototype to production or development todeployment, commercial software stacks will need to offer capabilities like real-time,fault tolerant, safety and security, and hence the commercial software ecosystem will
be needed to replace or augment the open source and proprietary software that iscurrently in development. These stacks will need to scale across verticals as well. In
other words, some portion of a software stack designed for a train can be used in a car.
How that is standardized and secured is key.
Lastly, it’s critical to consider some common base architecture that allows your designteam to pivot from open source to commercial SW as they reach deployment. This
reduces complexity and improves time to market through quick migration of workloads.

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How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Section 5
How to think at a platform level

To deliver on this vision and overcome the complexity issues, we have to not
only bring down the cost of compute but also deliver choice and flexibility todevelopers ideally with a support ecosystem that can deliver everything fromIP to algorithms to test and validation programs and protocols.
New technologies in the design of CPUs,
GPUs and ISPs will offer the crucial
steppingstones to enhance ADAS and IVI
capabilities in our vehicles, power new,driverless autonomy and enable more
intelligent, configurable production lines
in factories as we move into this new eraof autonomous decision making. Machinelearning, for example, will be a criticaltechnology running on these devices.
CPUs need to offer the ability to
run different workloads/application
footprints, allowing the deployment of
the same SoC compute architecture into

Ultimately, thisheterogeneous
flexibility will deliver
processing power thatwill enhance ADAS, IVI
and autonomy through:different domain controllers. They needto support features to help achieve both
ASIL D and ASIL B safety requirements,
allowing flexibility through its partitioning
features that enable the highest safetylevels when they’re needed, or highest
performance when they are not.
GPU technology needs to deliver “flexiblepartitioning” to allow the allocationof hardware resources at runtime –whether it’s between safe and non-safe
workloads or different types of workload.
Such technology must be designed toISO26262 and be ASIL B safety capable,

allowing the use of GPU resources forsafe human-machine interfaces and forthe heterogenous compute needed inautonomous systems.
Keeping with heterogeneity, developers
need an ISP that can scale to support
both real time and virtual cameras ofvarying types and offers a wide rangeof data output formats providing theflexibility to support both human andmachine vision applications such as
production line monitoring andquality control.

Enhanced safety
Enhanced Security
Machine learning	Thermally and power efficienttechnologies	Handing a wide range of data inputs
and formats and processing with ease

This is the foundation forscalability that forms the basisof a standardized solution
across applications, whether
it’s ADAS, IVI, autonomy, or
the factories that manufacture
the autonomous vehicles.

Shifting resources in real time06

How To Accelerate The Journey To Autonomous Vehicles

Market Deep Dive 2020

Conclusion
The development occurring today and that lies
ahead will transform the world as we know
it. Heterogeneous and scalable compute willaddress the range of processing capability
for differing workloads in a power-efficientmanner, in ways that will transform how wedesign autonomous vehicles and factories.

In automotive, by further enhancing the automation capabilities of ADAS and driverlesstechnology and applying it to operational domains where routes, speeds, location andenvironmental conditions can be bounded, we can accelerate the journey to deploying
autonomous vehicles.
For smart manufacturing, autonomy provides the intelligence to customize workflows,
whether that’s deciding what to manufacture next, or to optimize production line
logistics. Factory production lines will become reconfigurable, enabling flexibility tomeet the needs of differing products and their specific customisations, and this will add
value and cut cost.
The promise of these advancements and the lessons from these early developments
and relentless innovation from the ecosystem are the keys to delivering on the five key
pillars of autonomous systems: Safety, scalable compute, flexibility, reliability
and robustness.

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