How To Navigate theJourney to AutonomousFactories, & Supply Chains
Autonomous systems will require safety, scalable
compute, flexibility, reliability and robustness

How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020

Why you should read thisAutonomous workloads are changing the way we design
products such as autonomous and semi-autonomousvehicles. It’s an enormous opportunity to scale design withmore flexible, cost-effective methods to deliver products thatare 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

One of the most profound opportunities
to propel the digital world forward lies in
deploying autonomous workloads
They promise to deliver scalable devices
and systems that can safely decide andact for themselves to enable operational
efficiencies working alongside humans or
taking humans out of the loop, creatingtime that can be spent elsewhere.
Autonomous vehicles are the most visible
manifestation of autonomous workloads,
but the factory lines and supply chainsthat deliver them stand to gain just asmuch. In the factory, autonomy willlead to increasingly more efficient andintelligent robotic machines tied into
ever more sophisticated building andnetworking systems that will delivergains not only in manufacturing but inthe larger supply chain and logistics

operations. And autonomy provides
the intelligence to customize workflows, tooptimize production line logistics
and output.
This will not be an overnighttransformation; it will be a journey.
Autonomous workloads will needfundamental building blocks including
heterogeneous compute, safety, security
and machine learning, that are commonacross autonomous-based use cases.
While the journey will transform how wedesign and deliver powerful new systems
and applications, it won’t be easy. The
sheer complexity of a fully autonomous
system is mind-boggling. Despite this,we’re already seeing disruptive companies
take advantage of the technology in theindustrial sector, including new entrants
from non-industrial segments, such as
Amazon and Google.
In short, realizing a successful journey
to autonomous requires a fundamental
reconsideration of design, embracing
emerging technologies and methodologies.
Here, we outline some challenges
and opportunities in the autonomous
journey in industrial, based on a numberof interviews with domain experts weconducted to get their observations. The
following is based on those interviews.

01How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020

Section 1
What’s driving the need forautonomous workloads

For the industrial sector,
autonomous workloads are
yet another milestone on along road of technologicalinnovation dating back200 years to a period wheninnovators harnessedrivers to power mills.

Today, the need for autonomous decision-making workloads that are not dependent
upon human interaction is clear for a number of reasons.
Managers in the industrial sector want to deploy autonomous workloads to achieve
higher productivity and improved KPIs and better operational safety. They wantto make their factories more flexible (and therefore more profitable) in building
customized products more efficiently, adapting their product mix on the fly.
To transform their business, managers want to integrate their factories more tightly
with the wider supply chain and logistics providers. Here, the goal is one autonomouschain from ordering, to raw material requisitions, to last-mile delivery, taking humans
out of the loop.
In the industrial world, automatic guided vehicles (AGVS) will benefit from becoming
fully autonomous, albeit within a bounded environment. This area will benefit fromthe trickle down of fully autonomous vehicle research and will result in improved
efficiency and customization for factories. It will improve manufacturing by delivering
fast-moving consumer packaged goods (FMCGs), pharmaceuticals, food andassembly automation from high-speed production lines that will experience minimal
or no downtime or reprogramming.
But at present, factories can be outfitted with disparate systems and a dizzying rangeof software and hardware choices, which can make charting a path to autonomy
challenging. Most factories have some degree of automation, but retooling for new
products or expanded customer requirements can take operations offline for days orweeks at a time.

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How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020

Section 2Rethinking the compute and data challenge

To realize the vision,
we need to understandhow fundamentallydifferent autonomous
workloads are fromtraditional workloads

Autonomous workloads are defined byreceiving input and generating an output
in their simplest terms – the ability tosense and take intelligent action basedon the captured data. The magnitude
of workload is far greater thanks to theincorporation of ML or AI.
Autonomous is 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 autonomous
mobile robots (AMR) that are alerted
by the vision system that a task has
been completed and pickup is required.

Integration may also be required forother points along the supply chain.
For example, custom trim requests on avehicle can be relayed from the buyer ordealer up the supply chain to order andship what’s required.
In industrial machines, there are theenormous computational requirements
around capturing, fusing, analyzing andacting on vast (and different – cameras
and computer vision to GPS inputs)
amounts of data. Layers of sensor inputs
must be processed in real time in orderto predict situations that machines cananticipate and react to. The technology
is there, but the computational power
to run the algorithms that process

the data is what makes the difference.
That difference is defined by the SoC
responsible for processing, planning,and control workloads. This all requiresa level of redundancy that is not present
in other, more traditional workloads. It
can be managed efficiently by a single,
scalable architecture, however.
Configuring these systems is costly.
Compute alone can cost thousands ofdollars, while the sensors themselves
sometimes cost thousands of dollars.
Factory safety demands redundancy
across the range of cameras and sensors,
and that raises costs. And in general, themore autonomy, the more sophisticatedthe system, the more expensive it is.
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How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020

Section 3
How to achieve scale
The real value lies not just in delivering autonomy todevices but by scaling that to a larger context, be it afactory, a supply chain or, in the case of autonomousvehicles, an urban environment.
In the factory, for example, it’s not just theautonomy of one robot, but the collectionof smart devices within the factory that’s
required to autonomously decide what tobuild next and how to do it. This requires
orchestration of a collection of systems intiers of hierarchical autonomy all working
together. For example, autonomous
robots deliver raw materials or subassemblies to an autonomous line. This
all requires the right level of compute
within the device and system to ensure
efficiency, accuracy, reliability and safety.
In trying to deliver on this vision ofscalable autonomy, many adoptersstruggle with a high degree of manual
work that’s required up front to deliversystems. This includes the cost andcomplexity of silicon and algorithms. Then
there’s time-to-yield if: In a factory, if the
product being produced will command
high margins, then the line can afford
to be down for six months while it’s
being made autonomous. But that’s notpossible for low-margin or low-revenue
product lines; manual workflows will still
be required.
In addition, engineers worry about thecurrent state of AI capabilities – deeplearning and affordable vision andsensing technologies – are not yet where
they need to be to deliver the results
they want.

Today’s approaches and solutions
often require developers to bundle
off-the-shelf hardware just to get thehorsepower required for prototyping.
Going forward, these new systems
must leverage heterogeneous compute
not only to deliver flexibility, but tohelp reduce the power, size, cost andthermal properties while retaining theperformance needed to run autonomous
workloads. Heterogeneous compute will
be delivered for different stages in theworkload and include security enclaves
and safety islands to deliver trust.

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How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020

Section 4
Standard software that scales
For autonomous systems to succeed, there’s an entire
software stack and software ecosystem that needs tobe developed that abstracts many of the details about
how the underlying systems work from the people who
are thinking about the application level.

For example, it’s not going to make sense for every single program that runs on a given
piece of hardware to understand exactly what that the interface is to its network
accelerator and exactly how to communicate with it. “Hiding” that beneath a standard,
and leveraging a robust, open ecosystem with vast number of contributors will help
scale autonomous systems.
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. Autonomy
needs to balance openness without losing all “stickiness” in order to head off thedisruptors who propose totally open systems. A robust ecosystem can ensure this as
well as develop a frame to ease technology selection that helps match an algorithm to arange of hardware with ease.
Customers that have much more demanding safety requirements will requiresafety-certified RTOS and middleware solutions. All of these elements will have somevery demanding requirements as far as being able to achieve safety certifications suchas IEC61508.
The software portion of the design also depends on where you are in yourdevelopment. For example, as autonomous vehicles move from prototype to production
or development to deployment, there will be the need for commercial software stacks
offering capabilities like real-time, fault tolerant, safety and security, and hence thecommercial software ecosystem will be needed to replace of augment the open sourceand proprietary software that is currently in development.
It’s critical to consider some common base architecture that allows your design teamto pivot from open source to commercial SW as they reach deployment. This reducescomplexity and improves time to market through quick migration of workloads.
Software also needs to scale across verticals, so many companies can achieve
efficiencies as they design for different applications.

05How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020Section 5
The 360-degree approach

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 from IP
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 capabilities infactories and the supply chain to enable
more intelligent, configurable productionlines in factories as we move into this new
era of autonomous decision making.
CPUs need to offer the ability to
run different workloads/application
footprints, allowing the deployment of
the same SoC compute architecture into
different domain controllers. They needto support features to help to achieveboth SIL2 and SIL3 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 toIEC61508 and be SIL2 safety capable,
allowing the use of GPU resources forsafe heterogenous compute needed inautonomous systems.

Keeping with heterogeneity, developers
need an image signal processor (ISP)
that can scale to support virtual
cameras of varying types and offers
a wide range of data output formats
providing the flexibility to support bothhuman and machine vision applications
such as production line monitoring andquality control.

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How to navigate the journey to autonomous factories, & supply chains

Market Deep Dive 2020

Conclusion
The autonomous development
occurring today and in the future willtransform the world as we know it.

All the hardware required for autonomous systems needs to be considered in acomprehensive way to run the complex workloads that are needed. Heterogeneous andscalable compute will address the range of processing capability for differing workloads
in a power efficient manner. Enabling this more efficiently and cost-effectively will bestandardized chipsets, geared to specific purposes, that are available from various
vendors to give designers far greater freedom of choice, flexibility and peace of mind.
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 save cost. The larger supply chain will become much more efficient anddeliver products more quickly thanks to this technological transformation.
Harnessing waterways spawned the industrial revolution, which transformed theworld. Autonomous workloads, delivered in the manner described here, will deliver noless profound an impact on the industrial sector and civilization

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