AWS for Semiconductor and Electronics Design | Hsinchu, April 10

AWS for Semiconductor
and Electronics
Using Cloud for Design, Engineering, Manufacturing
April 10, 2014
David Pellerin, Principal Business Development Manager, HPC
Amazon Web Services

Agenda
13:30
AWS Cloud forIT Enterprise – OpeningRemarks and Case Studies
James Tien,Sales & Business Development Manager,Amazon Web Services
14:00
15:00
AWS Cloud for Design and Simulation – Case Studies in CAE and EDA
David Pellerin,HPC Business Development Principal,Amazon Web Services
15:15
16:00
Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA
16:00
16:45
MentorGraphics Design Collaboration
Julian Sun,Business Development Director,Mentor Graphics
16:45
AWS Kinesis – Big Data Management and Analytics in Manufacturing
Ken Chan,Solutions Architect,Amazon Web Services
17:15
ClosingRemarks and Q&A

AWS Semiconductor
James Tien
Sales and Marketing, Taiwan

8 Years Young
Amazon Simple Storage Service (S3) launched: March 14th 2006

Pace of Innovation
In 2013: 280 new services, significant features and updates
24
48
61
82
159
280

On average, AWS adds enough new
server capacity every day to support
Amazon’s global infrastructure when it was
a $7B business.

Q4 2006
Q1 2007
Q2 2007
Q3 2007
Q4 2007
Q1 2008
Q2 2008
Q3 2008
Q4 2008
Q1 2009
Q2 2009
Q3 2009
Q4 2009
Q1 2010
Q2 2010
Q3 2010
Q4 2010
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Over 1,500,000 peak
requests/sec
Amazon Simple Storage Service (S3):
Trillions of Total Objects

10 AWS Regions Worldwide
25 Availability Zones
Tokyo
Region
Sydney
Region
Singapore
Region
China
Region

Global Content Delivery Network
51 Edge Locations
Europe
Amsterdam (2)
Dublin
Frankfurt (3)
London (3)
Madrid
Marseille
Milan
Paris (2)
Stockholm
Warsaw
Asia
Chennai
Hong Kong (2)
Manila
Mumbai
Osaka
Seoul
Singapore (2)
Sydney
Taipei
Tokyo (2)
South America
Sao Paulo
Rio de Janeiro
North America
Ashburn, VA (3)
Atlanta, GA
Dallas, TX (2)
Hayward, CA
Jacksonville, FL
Los Angeles, CA (2)
Miami, FL
Newark, NJ
New York, NY (3)
Palo Alto, CA
Seattle, WA
San Jose, CA
South Bend, IN
St. Louis, MO

Compute Networking
Storage &
CDN
Database App Services Management
Amazon EC2
Amazon ELB
AutoScaling
Amazon WorkSpaces
Amazon Route 53
Amazon VPC
AWS Direct Connect
Amazon S3
Amazon Glacier
Amazon EBS
AWS Storage Gateway
AWS Import/Export
Amazon CloudFront
Amazon RDS
Amazon DynamoDB
Amazon Elasticache
Amazon RedShift
Amazon AppStream
Amazon CloudSearch
Amazon SWF
Amazon SQS
Amazon SNS
Amazon SES
Amazon Elastic Transcoder
Mobile Push
AWS IAM
Amazon CloudWatch
AWS Elastic Beanstalk
AWS CloudFormation
AWS OpsWorks
AWS CloudHSM
AWS CloudTrail
AWS Trusted Advisor
AWS Marketplace
AWS Premium
Support
AWS Professional
Services
AWS
Training
Over 40 Broad & Deep Services to Support Virtually Any
Cloud Workload
Analytics
AWS Data Pipeline
Amazon Kinesis
Amazon EMR

Hundreds of Thousands of Customers in 190 Countries

AWS Hong Kong and Taiwan Customers

Media Sharing Explosive traffic
accommodation
Consumer
social app
Ticket pricing
optimization
SAP &
Sharepoint
Securities Trading
Data Archiving
Marketing
campaign
Marketing web
site
Interactive
TV apps
Fast development
and deployment
R&D data
analysis
Machine Learning
system development
Big data
analytics
Customized
movie
suggestion
Disaster
recovery
Media streaming
Web and mobile
apps
Streaming
webcasts
Facebook
app
Consumer social
app
Every Imaginable Use Case
Global
game
service

Why are customers adopting cloud
computing?
15

On-Premises
Requires significant, up-front capital expense
Pay As
You Go
$0 to get started
1. Trade Capital Expense for Variable Expense
16

2. Lower Total Cost of IT
Scale allows us to constantly
reduce our costs
We are comfortable running a high
volume, low margin business
We pass the savings along to
our customers in the form of
low prices
42 Price
Reductions

Self
Hosting
Waste
Customer
Dissatisfaction
Actual demand
Predicted Demand
Rigid Elastic
Actual demand
AWS
3. You Don’t Need to Guess Capacity
18

4. Dramatically Increase Speed and Agility
Old World
Infrastructure in Weeks
Infrastructure in Minutes
Add New Dev Environment
Add New Production Environment
Add New Environment in Japan
Add 1,000 Servers
Remove 1,000 servers
Number of Instances 1,000
Instance Type M3 Extra Large
Availability Zone US-West-2b
Launch
aws.amazon.com/managementconsole
19

Experiment Often
Fail quickly at a low
cost
More Innovation
4. Increase Agility when Innovation is Fast and Low Risk
On-Premises
Experiment Infrequently
Failure is expensive
Less Innovation
20
Nearly $0
$ Millions

Data Centers
Power
Cooling
Cabling
Networking
Racks
Servers
Storage
Labor
Capacity Planning
Buy and install new hardware
Setup and configure new software
Build or upgrade data centers
Repeat investments to go global
Toil with scaling distributed systems
Pay massive margins
So you don’t have to …
5. Stop Spending $$$ on Undifferentiated Heavy Lifting
We take care of it…
21

AWS Cloud for
Electronics and
Semiconductor
Introduction and Case Studies
April 10, 2014
Amazon Web Services

Cloud for Scalable EDA
• Technical capabilities
• Business realities
Cloud for Secure Global Collaboration
• New, more innovative solutions for EDA users
• New opportunities for EDA software vendors
Cloud for Big Data Analytics
• For manufacturing yield analytics
• For improved Design-for-Manufacturing
Themes: for Today and the Future

Scalability:
Go wide, go large for faster time-
to-results at higher accuracy
Global Collaboration:
For enhanced IP security, more
efficient operations
Agility:
React quickly to changing needs
with flexible cloud capacity
Motivators for the Cloud

We understand this is a journey

Agenda
13:30
AWS Cloud forIT Enterprise – Overview and Case Studies
Tom O'Reilly, Head of Hong Kong & Taiwan,Amazon Web Services
14:00
15:00
AWS Cloud for Design and Simulation – Case Studies in CAE and EDA
15:15
16:00
Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA
16:00
16:45
MentorGraphics Design Collaboration
Julian Sun,Business Development Director,Mentor Graphics
16:45
AWS Kinesis – Big Data Management and Analytics in Manufacturing
Ken Chan,Solutions Architect,Amazon Web Services
17:15
ClosingRemarks and Q&A

AWS Cloud for Design
and Simulation
Why Scalability Matters for CAE and EDA
April 10, 2014
Amazon Web Services

Computer-Aided Design, Simulation, Analysis, Visualization
• Across industries, the trend is Simulation-Driven Design and Discovery
• Aerospace, semiconductor, automotive, civil engineering, energy exploration,
consumer products, finance, pharmaceuticals, many others
Examples in Design and Manufacturing
• Computer-Aided Design (CAD) including 3D models
• Finite Element Analysis (FEA) and Thermal Analysis
• Electronic Design Automation (EDA)
• Computational Fluid Dynamics
• Multi-physics simulations
• Molecular simulations for drug discovery
A Simulation-Driven World

A Collaborative World
Collaboration between functional groups
• Product Lifecycle Management
• Collaborative Design
• Concurrent Design
Collaboration for global teams
• Secure remote access to IP and applications

A Data-Intensive World
Managing big data for competitive advantage
• For design, engineering, production environments
• Quality and Yield Analysis
• Statistical Process Control
Processing
Input
Yield analysis
Manufacturing facilitymonitoring
In-field devicemonitoring
Logging
Log4J
Appender
push
to
Kinesis
ElasticMapReduce
Hive
Pig
Cascading
MapReduce
pull from

What are AWS Customers Telling Us?
“HGST is using AWS for a
higher performance, lower
cost, faster deployed
solution vs buying a huge
on-site cluster.”
- Steve Philpott, CIO
HGST application roadmap:
 Molecular dynamics
 CAD, CFD, EDA
 Collaboration tools for engineering
 Big data for manufacturing yield analysis
Every application
presents unique
challenges… some
technical, some
business

Cloud Provides Agility
Wasted Resources
Project Delays
Actual demand
Predicted Demand
Rigid On-Premise Resources Elastic Cloud-Based Resources
Actual demand
Resources scaled to demand
3 to 5 year architecture commitment Little or no architecture commitment

Maintaining an EDA cluster is expensive
Is it worth your organization’s time and effort?

Consider a typical big compute job… such as
ASIC timing simulation or mask verification

…for which a departmental cluster is too small, or
simply takes too long to complete…

You can run the job using a central shared cluster…

…if you can get through the job queue!
?

The Hidden Cost of Queues
Conflicting goals
• EDA users seek fastest possible time-to-results
• Simulations are not steady-state workloads
• IT support team seeks highest possible utilization
Result:
• The job queue becomes the capacity buffer
• Job completion times are hard to predict
• Users are frustrated and run fewer simulations
Fewer simulations = lost opportunity!
?

The Hidden Cost of Queues
This is what
100% utilization
looks like

On the cloud, clusters are created on-demand
and can be balanced dynamically for each job…

…with multiple clusters
running at the same time

Match the Architectures to the Jobs
Scale up and scale out…

Use automation to manage cluster
sizing and monitor jobs and costs
AWS Auto Scaling works
with existing HPC
scheduling software

Who Uses Cloud Today?
global enterprises, global applications

Worldwide Research and Development
“The Amazon Virtual Private Cloud was a unique option that offered an additional
level of security and an ability to integrate with other aspects of our infrastructure.”
“AWS enables Pfizer’s WRD to explore specific difficult or deep
scientific questions in a timely, scalable manner and helps
Pfizer make better decisions more quickly”
Dr. Michael Miller, Head of HPC for R&D, Pfizer
http://aws.amazon.com/solutions/case-studies/pfizer/

Supporting Innovation in Compliance-Sensitive Industries

Courtesy of Cypress Semiconductor
Supporting Innovation in Electronic Product Design

EM Field Simulations
for TRUETOUCH® Touchscreen Controllers– Cypress Semiconductor
3D FEM simulations SPICE
OUTPUT: sensor speed, SNR, and
signal disparity
OUTPUT: unit cell parameters in
respect to sensor design
 Finite-element mesh used for 3D simulations consists of over one million vertices
 Lack of computational resources can limit the capability to model complex
geometries and/or increase simulation time Courtesy of Cypress Semiconductor

MASTER Node 01
Virtual Private Cloud
Job 01: parameter set 2
Job 02: parameter set 2
Job NN: parameter set NN
Job submission
Accumulated
simulation results
 Simulations can be submitted as an array of jobs
that share the same executable and libraries,
different input parameters
 Result: simulation time reduced
from weeks to just hours
Node 02
Node N
EM Field Simulations
for TRUETOUCH® Touchscreen Controllers– Cypress Semiconductor
Courtesy of Cypress Semiconductor

US West
(Northern
California)
US East
(Northern
Virginia)
EU
(Ireland)
Asia
Pacific
(Singapore)
Asia
Pacific
(Tokyo)
AWS Regions (10)
AWS Edge Locations
US West
(Oregon)
South
America
(Sao Paulo)
Regions and Availability Zones
GovCloud
(ITAR Compliance)
Asia
Pacific
(Sydney)
China
(Beijing)

What Does Scale Mean in the Cloud?
18 hours
205,000 materials analyzed
156,314 AWS Spot cores at peak
2.3M core-hours
Total spending: $33K
(Under 1.5 cents per core-hour)

How do you Scale an EDA Cluster?
Actual demand
Predicted Demand
What size of cluster do you need?
• A different size of cluster is needed at different
points in the engineering process
• Pace of innovationwill depend on making the right
sizing decision
And what kind of cluster is it?
• Large memory?
• More and faster cores?
• Faster storage?
• Faster networks?
• What generation of processor?
• IT hardware is a long-term commitment – when is
the right time to buy?

AWS Has the Scale
to Constantly
Innovate

2006 2007 2008 2009 2010 2011 2012-2013 March, 2014
m1.small
m1.xlarge
m1.large
m1.small
m2.2xlarge
m2.4xlarge
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
cc2.8xlarge
cc1.4xlarge
cg1.4xlarge
t1.micro
m2.xlarge
m2.2xlarge
m2.4xlarge
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
cr1.8xlarge
hs1.8xlarge
m3.xlarge
m3.2xlarge
hi1.4xlarge
m1.medium
cc2.8xlarge
cc1.4xlarge
cg1.4xlarge
t1.micro
m2.xlarge
m2.2xlarge
m2.4xlarge
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
cc1.4xlarge
cg1.4xlarge
t1.micro
m2.xlarge
m2.2xlarge
m2.4xlarge
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
g2.2xlarge
hs1.8xlarge
m3.xlarge
m3.2xlarge
hi1.4xlarge
m1.medium
cc2.8xlarge
cc1.4xlarge
cg1.4xlarge
t1.micro
m2.xlarge
m2.2xlarge
m2.4xlarge
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
new
existing
EC2 Instance History
c3.large
c3.xlarge
c3.2xlarge
c3.4xlarge
c3.8xlarge
m3.medium
m3.large
i2.large
i2.xlarge
i2.4xlarge
i2.8xlarge
r3.large
r3.xlarge
r3.2xlarge
r3.4xlarge
r3.8xlarge
hs1.xlarge
hs1.2xlarge
hs1.4xlarge
deprecated
Increasing customer choice…

Performance Factors: CPU
• Intel Xeon E5-26XX v2 (Ivy Bridge) CPUs
• Available in AWS C3, R3, I2 instance types
• 2.8 GHz, Turbo enabled up to 3.6 GHz
• Intel® Advanced Vector Extensions (Intel® AVX):
• 256 bit instruction set extension
• Designed for applications that are floating-point (FP) intensive
• The “Ivy Bridge” microarchitecture enhances this with the
addition of float 16 format conversion instructions

C3: CPU-Optimized Instance Type
• 2.8 GHz Intel Xeon E5-2680v2 (Ivy Bridge) CPU
• Turbo enabled to 3.6 GHz
• Various instance sizes with 2, 4, 8, 16, 32 vCPUs
• From 3.75GiB to 60GiB RAM
• From 32GB to 640GB SSD
• High PPS, low-latency Enhanced Networking: over 1M PPS
• Supporting Cluster Placement Groups for all sizes

R3: Memory-Optimized Instance Type
• Multiple instances sizes with 2, 4, 8, 16, 32 vCPUs
• Up to 244 GiB RAM (~ 8GiB/vCPU)
• SSD Based Instance Storage
• High PPS, low-latency Enhanced Networking

I2: High-IOPS Instance Type
• Various instances sizes with 4, 8, 16, 32 vCPUs
• 30.5, 61, 122, 244 GiB RAM
• 16 vCPU: 3.2 TB SSD; 32 vCPU: 6.4 TB SSD
• 365K random read IOPS for 32 vCPU instance
• High PPS, low-latency Enhanced Networking

Performance Factors: Networks
• AWS proprietary, 10Gb networking
• Highest performance in .8xlargeinstance sizes
• Full bi-section bandwidth in placement groups
• No network oversubscription
• Enhanced Networking
• Availableon C3, R3, I2 (in VPC with HVM)
• Over 1M PPS performance, reduced instance-to-instance
latencies, more consistentperformance than earlier
generation AWS networks

Performance Factors: Accelerators
NVIDIA GPUs!
• For computing and for remote graphics
• CG1 and G2 instances
• GPU accelerators augment CPU-based
computing by offloading specialized
processing
• Performance gains depend on application-
level support

Today?
 Testing and development,patch testing, user training
 EDA vendor sales enablement via “test drives”
 Customer POCs, using real production examples
 Customer-managed production EDA
• With or without EDA vendor involvement
Tomorrow…
• Vendor-approved and documented cloud architectures for EDA
• Customer-approved security and compliance best-practices
• New EDA license models supporting extreme scalability
• New software architectures allowing faster time-to-results,
higher quality at reduced infrastructure cost
Cloud for EDA: Today and Tomorrow

1) Customer Managed Application Hosting
• Customer has account with cloud provider and manages virtual infrastructure
• Cloud used for batch jobs via cluster management software
• Customer can also remote login and globally collaborate using GPU instances
• Customer maintains traditional software vendor relationships
• Software vendor optionally offers license flexibility for scalable computing
2) Software Vendor Managed Application Hosting
• SaaS or hybrid model for acceleration of batch tasks, for example rendering
• Customer pays software vendor for cloud-hosted services
• Customer does not need to manage virtual infrastructure
Options for Software Licensing

Scale
Global Collaboration
Agility
Cloud Offers…
…with higher performance and lower cost
than on-premise HPC

Cost Benefits of HPC in the Cloud
On-Premise
HPC
Metered, Pay As You Go Model
Use only what you need,
using on-demand, reserved, or spot
Flexible
Capital Expense Model
High upfront capital cost,
high cost of ongoing support
Inflexible
Cloud-Based
HPC

Optimize Costs
by Combining Reserved, Spot, and On-Demand Instances
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Heavy Utilization Reserved Instances
Light RI Light RILight RILight RI
On-DemandSpot and
On-Demand
100%
80%
60%
40%
20%
Percentage of Peak Requirements Over Time

Using Cloud for Global
Collaboration
Use-Cases in CAE and EDA
April 10, 2014
Amazon Web Services

Cloud
Collaboration
is
Secure
Collaboration

Global Collaboration for Global Manufacturing
Cloud provides a
global, distributed,
secure, and scalable
environment for
collaborative design
and manufacturing

Collaboration is More Secure in the Cloud
Bring the users to the data, don’t send the data
to the users

Secure Remote Access
Data and computation hosted in a secure, customer-managed virtual private cloud, with
controlled access via a wide variety of client devices.
Virtual Private Cloud
Powered by NVIDIA GPUs

NVIDIA GRID K520 in AWS Cloud
Product Name GRID K520
GPUs 2 x GK104 GPUs
CUDA cores 3,072 (1,536 per GPU)
Core Clocks 800 MHz
Memory Size 8GB GDDR5 (4GB per GPU)
HW Video Encoder 2x h.264 (1 per GPU)
Power Consumption 225W
Supported APIs
OpenGL 4.3, DirectX 9, 10, 11, CUDA
5.5, OpenCL 1.1, NVFBC, NVIFR,
NVENC

Application Streaming Middleware

• Application
Streaming
• Remote
visualization
• Thin client 3D
applications
Amazon AppStream

Thin Client Remote Collaboration
Calgary Scientific PureWeb™
www.calgaryscientific.com/resolutionmd/web/
demos.getpureweb.com/

Remote Evaluation and Training
MentorGraphics® Virtual Labs on AWS
www.mentor.com

AWS Test Drive
• Provides softwarevendors with a
controlled, secure, convenient
environment for product
evaluation and training
• Any application listed on AWS
Test Drive is available for
purchasefrom the ISV, and can
be deployed on AWS if desired

Cloud-based PLM with fast deployment
and simplified scalability
Dynamically scale PLM infrastructure
up and down based on project needs

Design Collaboration
Featuring Mentor Graphics
April 10, 2014
Julian Sun, Business Development Director, Mentor Graphics
Amazon Web Services

G2 Supports AWS Test Drive
Mentor Graphics HyperLynx® PI

Big Data Management
For manufacturing
April 10, 2014
Amazon Web Services

Motivator: reduce the time spent searching for data
Aggregate data to a common platform, with common access tools
Improve manufacturing yields by accessing more data in a more timely manner
Speed up the yield improvement ramp up on new products
Improve steady state yield on existing products
Provide end-to-end visibility into:
Every test, every diagnostic
Data generated from all components of a product
Data generated internally, and from field deployments
Big Data Analytics in Manufacturing

Scenarios Accelerated Ingest-Transform-Load Continual Metrics/KPI Extraction Responsive Data Analysis
Software/
Technology
IT server , App logs ingestion IT operational metrics dashboards Devices / Sensor Operational
Intelligence
Digital Ad Tech./
Marketing
Advertising Data aggregation Advertising metrics like coverage, yield,
conversion
Analytics on User engagement with
Ads, Optimized bid/ buy engines
Financial Services Market/ Financial Transaction order data
collection
Financial market data metrics Fraud monitoring, and Value-at-Risk
assessment, Auditing of market order
data
Manufacturing Production line and field repair data
collection and aggregation
Yield and failure analysis, batch and
real-time
Production monitoring systems,
embedded controllers, device logs
Consumer Online/
E-Commerce
Online customer engagement data
aggregation
Consumer engagement metrics like
page views, CTR
Customer clickstream analytics,
Recommendation engines
Scenarios Across Industry Segments
1 2 3

Metrics from HGST Big Data Platform pilot project:
Collecting >2M manufacturing/testing input files daily
Collecting from ~500 tables across 6 databases  tens of millions of records daily
HGST’s BDP is demonstrating early benefits:
Example: HGST
Development Engineer: demonstrated the joining of data sets for detailed
logistics tracking—analyses that is very difficult to conduct with current
systems
Ops Engineer: a recent production issue required detailed historical data. Current systems
did not have the required retention for this data. However, the team was able to pull the data
from the BDP in minutes, as opposed to 3+ weeks to pull the data from tape archive
Development Engineer: obtained technical data from the BDP in
hours as opposed to 3+ weeks to pull from tape archive
DATA SEARCH
PARTIES
YIELD

Kinesis Architecture
Amazon Web Services
AZ AZ AZ
Durable, highly consistent storage replicates data
across three data centers (availability zones)
Aggregate and
archive to S3
Millions of
sources producing
100s of terabytes
per hour
Front
End
Authentication
Authorization
Ordered stream
of events supports
multiple readers
Real-time
dashboards
and alarms
Machine learning
algorithms or
sliding window
analytics
Aggregate analysis
in Hadoop or a
data warehouse
Inexpensive: $0.028 per million puts

Sending & Reading Data from Kinesis Streams
HTTP Post
AWS SDK
LOG4J
Flume
Fluentd
Get* APIs
Kinesis Client Library
+
ConnectorLibrary
Apache Storm
Amazon Elastic
MapReduce
Sending Reading

Possible Use-Case in ASIC Production
Processing
Input
Yield analysis
Manufacturing production monitoring
and logging
Logging
Log4J
Appender
push to
Kinesis
ElasticMapReduce
Hive
Pig
Cascading
MapReduce
pull from

107
Easy Administration
Managed service for real-time streaming
data collection,processingandanalysis.
Simply create a new stream,set the desired
level of capacity,andlet the service handle
the rest.
Real-time Performance
Perform continual processingonstreaming
big data. Processinglatencies fall to a few
seconds,comparedwiththe minutes or
hours associatedwithbatchprocessing.
High Throughput. Elastic
Seamlessly scale to matchyour data
throughput rate and volume. Youcaneasily
scale up to gigabytes per second. The service
will scale up or downbasedon your
operational or business needs.
S3, Redshift, & DynamoDB Integration
Reliably collect,process,andtransformall of
your data in real-time & deliver to AWS data
stores of choice,withConnectors for S3,
Redshift,and DynamoDB.
Build Real-time Applications
Client libraries that enable developers to
design and operate real-time streamingdata
processingapplications.
Low Cost
Cost-efficient for workloads of any scale. You
canget startedby provisioninga small
stream,and pay low hourly rates only for
what youuse.
Amazon Kinesis: Key Developer Benefits

AWS for Semiconductor and Electronics Design | Hsinchu, April 10

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AWS for Semiconductor and Electronics Design | Hsinchu, April 10