The document discusses a leadership session on using cloud technologies to accelerate innovation for intelligent, connected products in the high-tech and semiconductor industries. It highlights key workloads like electronic design automation (EDA) and examples of companies innovating faster on AWS through more efficient EDA workflows, faster software testing, and reduced product development times.

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Leadership Session: Hitech/Semiconductor
Increasing the Pace of Innovation for Intelligent,
Connected Products
David Pellerin
Head of Worldwide Business
Development, Infotech/Semiconductor
M F G 2 0 1 - L
Mark Duffield
Global Tech Leader,
Semiconductor

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Goals
Highlight top workloads and trends across the electronic
product ecosystem
Dive deeper into industry- and workload-specific applications
Highlight recent customer successes with cloud-based
hitech/semiconductor innovation

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A complex industry …
Design and
verification
Wafer
production
Chip
packaging
Assembly Product
integration
Product
distribution
…with many opportunities for cloud-accelerated innovation

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Why cloud for the semiconductor industry?
Innovating at cloud speed
requires constant optimization
Optimization requires
digital transformation
Digital transformation requires cloud
You can’t optimize what you
can’t measure, and you can’t
measure what you can’t connect
“
”

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Intelligent, connected products
CloudEdge
Model deployment
Connected devices
Actionable insights
Data aggregation
Model development
Model training

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Integrated
Client
Intelligent, connected devices require high-quality plumbing
Enterprise applications
Enterprise
Users
Corp Apps
Amazon
QuickSight
Amazon
EMR
Amazon
Redshift
Amazon
S3
Machine
Learning
AWS Lambda
All
AWS
Cloud
Device
Shadow
Rules
Engine
AWS IoT
Core
Certificate
Authority
AWS IoT Device
Management
IoT Users
Over-the-Air
(OTA) Updates
Analytics
Data Store
Data
Pipelines
Templated
Reports
Batch Fleet
Provisioning
Real-Time
Fleet Index &
Search
AWS IoT Device
Defender
Ad-Hoc & In-
Depth Analysis
Risk Mitigation
Monitor Device
Behavior
Alerts
Message
Broker
Audit Device
Configurations
Amazon
Kinesis
AWS IoT
Analytics
AWS IoT 1-Click
MQTT
MQTT
Endpoints Gateway/PLC
Device
Shadow
Lambda
Functions
Local Comms Long-range Comms
Amazon FreeRTOS
Certificate
Authority
Local
Resources
IoT SDK
OPC-UA
MQTT
Edge
Users
Cert
WiFi
MQTT
Edge
Amazon FreeRTOS
Snowball Edge
AWS Greengrass
MQTT
AWS Greengrass
Message
Broker
Protocol
Adapter
EDGEDEVICES
CLOUD
OTA
OTA
AWS

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Top Cloud use-cases for semiconductor?
Electronic design
automation (EDA)
High performance
computing (HPC)
Artificial intelligence
and machine learning
(AI/ML)
Internet of Things
(IoT)
Product lifecycle
management (PLM)
Software
regression testing
Big data/
data lake
SAP

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• Security of IP, workflows, and tools
• Performance—compute, memory, networks, storage
• Automation and cluster/job/license management
• Remote graphics for interactive applications
• EDA and IP vendor support
• Foundry support
• Cost optimization
Focusing on electronic design automation
Important factors for successful EDA at scale

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Solution components for cost-optimizing EDA
Storage
Amazon EBS
Amazon EFS
Amazon S3
Networking
Enhanced
networking
Placement
groups
Automation &
orchestration
AWS Batch
AWS ParallelCluster
NICE EnginFrame
Visualization
NICE DCV
Appstream 2.0Compute
EC2 instances
(CPU, GPU,
and FPGA)
EC2 Spot
Auto Scaling
Partners
ISVs and SIs

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Remote backup Remote sites
The traditional CAD/CAE/EDA stack has limits
Corporate data center
Shared file storage
HPC clusters
License managers and cluster
head nodes with job schedulers
3D graphics remote desktop servers
Storage cache
Remote graphics
workstations

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Remote backup Remote sites
The traditional CAD/CAE/EDA stack has limits
Corporate data center
Shared file storage
HPC clusters
License managers and cluster
head nodes with job schedulers
3D graphics remote desktop servers
Storage cache
Remote graphics
workstations
CHALLENGES
Inflexible
Poor utilization
Multi-year life cycle management
Are you secure?

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Migrating workloads such as EDA to AWS
Virtual Private Cloud on AWS
3D graphics
virtual workstation
License managers and cluster
head nodes with job schedulers
Cloud-based, auto-scaling EDA clusters
Shared file
storage
Storage cache
AWS
Snowball
AWS Direct
Connect
Encryption everywhere—with your own keys!
VPC
Corporate
data center
On-premises
HPC resources
Thin or zero client
—no local data—
Amazon S3
and Amazon Glacier
Third-party IP providers
and collaborators
Machine learning
and analytics
Third-party emulation
providers

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ThinkBIG
What if you could launch
1 million concurrent
verification jobs?
C P U C O R E S O V E R T I M E
Product development cycle
Think BIG
Faster design throughput with rapid, massive scaling
Scale up when needed, then scale down
• In a traditional EDA datacenter, the only
certainty is that you always have the wrong
number of servers—too few, or too many
• Every additional EDA server launched in the
cloud can improve speed of innovation—
if there are no other constraints to scaling
• Overnight or over-weekend workloads
reduced to an hour or less

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General purpose
and burstable
Compute
optimized
Storage and
I/O optimized
Memory
optimized
GPU
graphics
GPU and
FPGA compute
AWS compute instances
T3
G3
EG
G2
F1
P3
P2
M5
M4
I3 H1
D2C5
C4
Z1d X1
R5
R4
Z1d
T2
M5a

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Compute instances for sample EDA workloads
T3
G3
EG
F1
P3
M5
I3
C5
Z1d X1
R5
Z1d
Physical Synthesis
Formal Verification
Power and Timing Analysis
Design Rule Check
Emulator Builds
RTL and Analog Simulations
NFS Storage
License Servers
IP development
Physical layout
PCB design
(graphics)
Electromagnetics

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z1d instances—optimized for EDA
• Sustained all core frequency of up to 4.0 GHz
• 6 different instance sizes with up to 24 cores (48 vCPUs) per instance
• 16:1 memory to core ratio and up to 384 GiB of RAM
• Includes local NVME storage
• Optimized for EDA and other high performance workloads
Z1d
Model vCPU Memory (GiB) Instance storage (GiB) Networking performance EBS bandwidth
z1d.large 2 16 1 x 75 NVMe SSD Up to 10,000 Mbps Up to 2,333 Mbps
z1d.xlarge 4 32 1 x 150 NVMe SSD Up to 10,000 Mbps Up to 2,333 Mbps
z1d.2xlarge 8 64 1 x 300 NVMe SSD Up to 10,000 Mbps 2,333 Mbps
z1d.3xlarge 12 96 1 x 450 NVMe SSD Up to 10,000 Mbps 3,500 Mbps
z1d.6xlarge 24 192 1 x 900 NVMe SSD 10,000 Mbps 7,000 Mbps
z1d.12xlarge 48 384 2 x 900 NVMe SSD 25,000 Mbps 14,000 Mbps

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z1d performance for a leading edge CPU design Z1d
• Leading edge CPU block
• Advanced FinFET node
• ~3GHZ clock speed
• Completed with industry leading
EDA product: IC Compiler™ II
0
5
10
15
20
Hours
AWS Compute Instance
IC Compiler II on AWS
“Using the latest z1d instance from AWS,
we were able to speed up placement and
routing of a leading edge, FinFET CPU
design by 35%, using our flagship IC
Compiler II product.”
Deirdre Hanford, Co-General Manager
Synopsys Design Group
R4
z1d
6 Hours Faster

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Optimize using AWS storage options
Amazon Elastic File
System (Amazon EFS)
Object
Data transfer
AWS Direct
Connect
ISV
Connectors
Amazon
Kinesis
Data Firehose
Storage
Gateway
S3 Transfer
Acceleration
AWS
Snowball
Amazon
CloudFront
Internet/
VPN
BlockFile
Amazon
S3/S3-IA Amazon Glacier
Amazon Elastic Block
Store (Amazon EBS)
Amazon EC2
Instance Store

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Optimizing storage dataflow
Ingress
Egress
Life cycle
policiesEC2 instance
EFS EBS+EC2 Instance
store
Or other mounted shared
file system
Data transferAWS
AWS Direct Connect
ISV Connectors
Amazon Kinesis
Data Firehose
Storage Gateway S3 Transfer
Acceleration
AWS Snowball
Amazon
CloudFront
Internet/VPN
Corporate
data center
Object, block, file storageAWS
Amazon
S3/S3-IA
Amazon
Glacier

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Key elements of cost optimization
 Scale-up, scale-down – for compute and storage
 Right-size AWS resources for specific applications
 Immediate refresh to new instance types, new storage options
 Improve EDA license utilization – with reduced IT constraints
 Use cloud pricing models – including Reserved, Spot, On-Demand
 Be well-architected – Cloud is not the same as traditional EDA IT

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Optimize using EC2 pricing models
per-second billing
substantially less
?
discount

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Optimize using EC2 pricing models
Reserved Instances
Reserved Instances
Spot
Reserved Instances
On-Demand Instances
On-Demand Instances
Conservative:
Optimized:
On-Demand Instances
Spot
Spot
Optimized with scale-out (magnify the peak):

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EDA vendor and foundry support?
The cloud is pervasive and will fundamentally influence silicon design. TSMC
is the first foundry to collaborate with design ecosystem partners and cloud
providers to enable designs in the cloud.
“
” – Cliff Hou, vice president of
technology development at TSMC

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AWS partners in electronic design automation

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Cloud products for Cadence customers
Cloud products today for the engineering challenges of tomorrow
Cloud-enabled products to run in your
cloud environment with Cloud
Passport
CLOUD READY
Cloud-optimized products that run in
a fully supported and Cadence-
managed, ready-to-go cloud design
environment.
HDS CLOUD
Cadence® HDS Cloud includes:
• Licensed software and support
• Cloud-optimized services
• CAD and IT infrastructure support
• PDK and foundry expertise
• Complete security support
Customer
Managed
Cadence
Managed

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Synopsys Cloud Solutions
 Cloud scaling for peak usage and full-flow workloads
 Robust environment to accelerate project cycle times
https://www.synopsys.com/solutions/cloud.html

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Emulation Cloud support

31. NXP Semiconductors N.V. is a semiconductor
manufacturer, listed on the NASDAQ since
2010 and headquartered in Eindhoven, the
Netherlands. It enables secure connections
and infrastructure for a smarter world,
advancing solutions that make lives easier,
better and safer.
AWS and NXP case study, 2017
WORKLOAD: Electronic design automation
Meeting capacity needs
for semiconductor R&D
Need for EDA performance
“During the IaaS provider selection,
AWS had by far the widest range
of high performance compute (HPC)
solutions and was the most advanced
in terms of functionality.”
“At the start, in 2012, R&D IT built a
dedicated team, five staff who spent about
50% of their time on the project. The
team operated separately in an agile way
from the rest of R&D IT and were told to
work outside of all existing structures.”
“The free, agile, and iterative way of
development resulted in a high speed
of development for cloud-based
EDA workloads.”
“
”

32. Running on-demand, higher-capacity
regression testing on AWS was key to lowering
the variability of test run times. … We can now
get instant access to compute and memory
resources, which reduces waiting time and
improves developer productivity.
Ambs Kesavan
Software engineering and DevOps director
Xilinx
WORKLOAD: Software regression testing
Faster regression testing
Improved turnaround time
By using AWS for regression testing,
Xilinx improves turnaround time and
predictability of test runs during peak
load cycles
Resource contention
Xilinx also eliminates the challenge
of queue contention now that related
jobs can be provided with on-demand
clusters customized for their compute
and memory needs
Performance with predictability
Xilinx developers now have zero
wait time for compute resources,
and the organization has 100%
predictability for return of test results
“
”

33. MediaTek designs and develops silicon wafers
for wireless communications and digital
multimedia solutions. The Taiwan-based
company uses AWS to deploy its services
internationally, giving its IoT developers access
to the company network, so they can access
dashboards, manage devices, and upgrade
firmware remotely. By using AWS, MediaTek
has reduced its time to development by 50%
and expanded globally.
WORKLOAD: IoT and smart devices
Faster development
of smart products
Faster development times
By using AWS for development and
testing of IoT solutions, Mediatek
can more effectively manage their
globally distributed teams and get
to market faster
Create cloud-connected services
Mediatek is an AWS IoT partner offering
the Linkit One kit with Wi-Fi, GPS, BLE,
GPRS, as well as sensors and actuators
running AWS IoT
“
”

34. LSIS provides power system and automation
solutions for Smart Energy. By using AWS and
Rescale, LSIS has reduced the time needed to
develop complex energy infrastructure products.
WORKLOAD: HPC for Engineering
Faster Innovation in
Smart Energy
Faster simulation turnaround
times
Running engineering simulations at-
scale with up to 10 times more
computing power, and 5 to 15 times
better analysis time, depending on the
simulation model; running up to 100M
cell CFD models for full analysis of high-
voltage transformers
Reduced costs
Using EC2 Spot Instances and Rescale
ScaleX for improved software license
utilization, and reduced overall cost – up
to 34% savings comparing to on-
premise costs
“
”

35. The IT organization has been driving a massive
digital transformation and optimization of
business capabilities across the organization.
IT has been leading these changes by creating
rich environments for the data to thrive,
ensuring improvements in productivity
and collaboration across the massively
global organization.
Steve Phillpot, CIO
Western Digital
Transformation
Big data platform (BDP)
Data from across global manufacturing
sites are collected into a cloud-based
big data platform, enabling operational/
logistics tracking of millions of hard drives
produced each year and allowing analysts
to visualize data across JMP, Tableau,
IBM SPSS, and SAS
HPC for design and engineering
Cloud-based HPC, the foundation for
future storage architecture analysis,
accelerates product optimization, using
clusters of CPUs and GPUs to perform
millions of drive-head and disk interface
simulations, and to improve storage
magnetics product capacities
“
”

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Our own journey—our own transformation
20152014
Hybrid Model:
Native AWS (new projects)
Native AWS for overflow
(existing projects)
Shorten Si development
time
AWS
“One Team”
Acquisition of
Annapurna
On-prem data center
On-prem Labs
All-in on AWS:
Lift & shift architecture
Four different teams
multiple regions
Multiple end-to-end
silicon projects using AWS
2016 2017 Today
Multiple
Teams
Multiple
EDA
Environments
All-in on AWS:
Increased productivity via
native AWS services:
• Containers
• Batch
• EFS and S3
• z1d, R5, C5, X1e

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The 4th Industrial Revolution
Industry 1.0
Mechanical production
powered by water and
steam
Industry 2.0
Mass production based
on the division of labor
and powered by
electrical energy
Industry 3.0
Electronics and IT for
further automation and
disaggregation of
production
Industry 4.0
Cyber-physical
production systems
18th Century 20th Century 1970s Today

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Industry 4.0 - what are the technical enablers?
Autonomous
Robots
Simulation
Vertical & Horizontal
Integration
Industrial Internet of
Things
CybersecurityCloud
Additive
Manufacturing
Augmented
Reality
Big Data
& Analytics
Cloud is one of the
9 standalone enablers
Cloud is the central
innovation enabler across
all disciplines and
industries

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Machine learning and IoT for semiconductors
Applications throughout design and production
EDA …
• Intelligent local and
global routing
• Timing analysis and DRC
• Simulation parameter selection
• Design flow optimization
• And more
Foundry …
• Lithography optimization
• Wafer inspection
• Yield and failure prediction
• Predictive maintenance
• Supply chain management (SCM)
• And more
Engineering
& operational
DATA
Ingest ConsumeStore Analyze
Engineering
& operational
INSIGHTS

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Data lake is important for smart manufacturing
Secure,
cost effective
storage in S3
Amazon
S3
Amazon
Athena
Amazon
QuickSight
Amazon
EMR
Amazon
Redshift
Amazon Kinesis
Data Firehose
AWS
Direct Connect
AWS
Snowball
AWS Database
Migration Service
Security Token
Service
Amazon
CloudWatch
AWS
CloudTrail
AWS Key
Management Service
Protect and secure
Use entitlement to ensure data is secure and users’ identities are verified
Processing and analytics
Use predictive and prescriptive
analytics to gain better understanding
Data ingestion
Get your data in Amazon S3 quickly and securely
Amazon
DynamoDB
Amazon
ES
Amazon
API Gateway
AWS Identity and
Access Management
Amazon
Cognito
Catalog and search
Access and search metadata
Access and user interface
Give your users easy and secure access

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Modern data architecture
Insights to enhance business applications, new digital services
Ingest
Internet
interfaces
AWS Direct
Connect
AWS Database
Migration
Amazon Kinesis
Internet of Things
Scale (Batch)
Stream analysis
Amazon EMR
Speed (Real-Time) ML/Auditing
Event capture
Amazon Kinesis
Raw data
Amazon S3
ETL
Amazon EMR
Staged data (data lake)
Amazon S3
Advanced analytics
MLlib
Data scientists
Data analysts
Business users
Engagement
platforms
Automation/Events
Amazon
Machine
Learning
Amazon
S3
Amazon
Athena
Serving
Direct query
Amazon Athena
Schemaless
Amazon ElasticSearch
Semi/Unstructured
Amazon EMR
Data warehouse
Amazon Redshift
Legacy apps
Amazon RDS
Near-zero latency
Amazon DynamoDB
Data
sources
PLC
Cameras
Sensors
Actuators
AWS IAM AWS KMS
AWS
CLOUDTRAIL
AWS
CLOUDWATCH

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• Overall equipment effectiveness [OEE]
• Test time optimization
• Reduced cycle times
• End-to-end yield management
• Process shifts
• Yield excursions
• Bin yield prediction
• Defect rate reduction
• Outlier detection & failure prediction
• Quality exposure risks
• Failure analysis
• Commonality analysis
• Repeatability & reproducibility studies
• Device characterization & correlation
• Zonal & root cause analysis
• Device classification

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Cloud enables secure collaboration on data
Design and
Verification
Wafer
Production
Chip
Packaging
Assembly Product
Integration
Vertical & Horizontal
Integration

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Additional sessions of interest

46. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
David Pellerin
dpelleri@amazon.com
Mark Duffield
duff@amazon.com

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