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Collaboration
with your
University
Industry Centers of Excellence
Centers of Excellence for Innovation and Expertise
IBM is teaming with universities, startups, ISV’s
and industries to help develop Several Centers of
Excellence for Innovations and developing
expertise for real-world opportunities
AI & HPC Center of Excellence
Data Science Center of Excellence
Industry 4.0 Center of Excellence
Quantum Computing Center of
Excellence
3
Background and Motivation
The IBM CoE Labs will play a major role in the research and
development commercial and industrial development of
emerging technologies
There is a strong need for research and development activity
in these domains:
– Encouraging academic-industry partnerships
– Cross-disciplinary and collaborative research
– Making technology accessible to non-technical
business students
– Enabling faculty-technologist interaction and learning
– Enabling startups ,ISVs and industries to use the platform
to innovate in ways that improve the World condition
.
Technologies and Partners
4
2 Year Developmental Timeline
a) IBM Academic
Initiative partnership
b) OpenPOWER/IBM
Servers and
Accelerator for
Solutions
Developments
c) Technical Projects
deployment
d) Review of progress
in technical projects,
lab coursework
e) Workshops/Webinars
Advantages for Faculty & Students
Talent and Skills: (Remote Interns; Skills and Training)
Students and Research scholars will start working on the
advanced technologies will enable them to work on many
applications
Publications and Mindshare: (Press releases, Articles,
and Publications; Conferences and Events)
1. Conference Paper on software-based application
research /development in 6 months
Intellectual Capital: (Patents, Open source; Prototypes,
Demos; Curriculum; Student projects, Theses)
1. Prototype building of many research problems using
software-centric approach (hardware-centric baseline
implementation almost getting completed)
2. Potential to file disclosures
Opportunities: (Seed revenue; Leverage other funding;
Build ecosystems; Build government/client relationships)
1. Once software-centric solution available with
comparable performance using latest technologies , your
team would create prototypes which can be demonstrated
to several colleges
Our team of collaborators
WE STRIVE FOR YOUR
EXCELLENCE
Condensed matter physicist and
quantum technologist
Scientific Program Manager for
HPC/Supercomputing at NVIDIA
Professor , The Ohio State University
Associate Professor at The National
Institute Of Engineering, Mysore
CEO & Co-Founder at Appentra. High
Performance Computing Software
Research Associate Professor and
Director, Performance Research
Laboratory, University of Oregon
Executive IT Architect, IBM Systems
JACK C WELLS Dhabaleswar K (DK) Panda
Christopher Coleman SAMEER SHENDE CLARISSE HEDGLIN
ABHINANDAN SP
Manuel Arenaz
Assistant Professor (Computer
Science),
University of Wisconsin-Platteville
Arghya Kusum Das
Comprehensive Training on Data & Data
Modelling
8 MAJOR CONCEPTS & 32 MODULES
DATA SCIENCES
Data Science Training curriculum
10
Data Science Training curriculum
Comprehensive Training on Data & Data
Modelling
8 MAJOR CONCEPTS & 5 CASE
STUDIES
AI TECHNOLOGIES
AI TECHNOLOGIES curriculum
13
AI TECHNOLOGIES CURRICULUM
14
AI TECHNOLOGIES curriculum
1
4
3
2
5
Industry 4.0 Curriculum
Centre OF Excellence
Topics
Covered
Within
Training
Industry 4.0
Cyber Physical Systems (CPS)
Big Data & Analytics
in Industry 4.0
Smart Factory & Smart
Products
Additive Manufacturing
Computational Vision
• Introduction to Industry 4.0
• Enablers of Industry 4.0
• Scope of Industry 4.0
• Examples of Applications of Industry 4.0
• Realtime requirements for Application of Industry 4.0 in the Global
Scenario
• What are Cyber Physical Systems (CPS)
• What are the use of CPS in Industry 4.0
• Examples of Cyber Physical Systems
• One used case for Discussion
• One Used case for development/implementing
(Optional)
• Introduction to Data Science
• Python for Data Science and Data Analysis
• Data Visualization (Optional Softwares)
• Introduction to Machine Learning
• Introduction to Deep Learning
• Project (Optional)
• What is a Smart Factory
• What are Smart Products
• How are Smart Products related to Smart Factories
• Advantages of Smart Products
• Industry 4.0 and Smart Factories
• Use Cases and Discussion
Project (Optional)
• Additive Manufacturing(AM) Concepts
• Incorporation of AM in Industry 4.0
• Hands-On project on AM (Optional)
• Application based solution through AM
• Use cases from Global Applications
• Project (Optional)
• Introduction to Computational Vision
• Programming for Computational Vision
• Practical Project for Implementation
• Suggestions/Support for Final Year research
scopes (Optional and as required)
• Support for Advanced Research
• Designing & Programming of the Web of
Things
• AI and Machine Learning for IoT Big Data
• Case Study Implementationsfor Industry
4.0
• Introduction to the Internet of Things
• Design & Functioning of Wireless IoT
Technologies (softwares introduced)
• Sensors & Technologies used for IoT
• Global IoT Use Cases
Internet of
Things
Ideal place to connect, learn more and track
trends
AN IDEAL PLACE TO CONNECT WITH QUANTUM COMPUTING
WORLD
QUANTUM HUB
Highlights of Quantum
Hub
Regular online workshops and
information sessions featuring panel
discussions will experts in Quantum
Computing.
All the latest news on technology
developments including new quantum
platforms, breakthroughs on existing
systems as well as use cases and real-
life applications.
Access to our database of market
analytics including our quarterly report
on emergent and established players.
Ø
Ø
Ø
19
Proposed AI cloud setup and specifications - Hardware
College Ethernet Network
4
4
College Ethernet Network
2 IBM AC922 System
128 GB Memory
2 TB Hard drive
40 Cores Power 9 Processor
NVLINK-2 nVidia GPUs – 8
1 IC922 System with 2 T4 GPUs
1 Login Server
applications
1 EDR Infiniband swtich
16 TB Storage Sub System
IB ConnectX cards
Edge Compute devices
1 RACK ( Which can fit in )
20
The AC922 has 2 POWER9 sockets, each providing extreme levels of IO
and memory bandwidth. As an example, in the configuration proposed, each
socket will communicate with two Nvidia V100 GPUs directly utilizing the
300GB per second NVLink2 bus connections on each POWER9 socket. In
addition, sockets have high memory bandwidth, PCIe Gen4 bandwidth,
and a high bandwidth SMP interconnect.
21
22
AI Lab users
AI Lab Software Components
University Use Cases and Scenarios of
Proposed AI Lab
AI Cloud at Universities
24
Use Case 1 : Students (daily use) requests for compute resource
Basic ML/DL exercises
Login to web portal
with “Student”
profile; browse
service catalog.
Select and request
for desired image,
and usage period
eg. MS Office with
Windows for 2
hours.
Login and access
Docker Container
(Remote Desktop)
AI Cloud Portal AI Cloud
Infrastructure
User / profile
authentication
Service request
processing &
approval
VM & storage
created according to
request
OS deployed into
Docker Container
Application image
deployed into Docker
Container
Login info sent to
user via email
Docker Container
with PowerAI image f
Downloads
completed work
into laptop and logs
off.
Resources made available to
students for daily use will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
Students
Students login from
anywhere within the
UM LAN. Cloud portal
is accessed via a web
browser.
Application and OS
images have to be
preconfigured by the
cloud admin before
use.
25
Use Case 2 : Final Year Students requests for compute resource for
AI Projects
Login to webportal
with “FY” profile;
browse service
catalog.
Select and request
for desired image,
and usage period e
Login and access
Docker Container
(Remote Desktop)
AI Cloud Portal Cloud Infrastructure
User / profile
authentication
Service request
processing &
approval
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
Downloads
completed work
into laptop and logs
off.
Resources made available to
final year students will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
FY Students
Students login from
anywhere within the
UM LAN. Cloud portal
is accessed via a web
browser.
IP address for VM
deployed within same
subnet. Students
access from laptop.
VM and Storage size :
2-4 cores, 4GB RAM, 10GB
storage
RHEL
Jetson Nano
VM for the FY student will be
operational until the expiration
date stated in his request.
26
Use Case 3 : Final Year Students creates own application image,
and shares image with other FY students.
Student to seek
approval from
Cloud Admin to
create new app
image in cloud infra
New image is
displayed in the
service catalog
Other FY students
proceed to request,
access and use
new application (as
per Use Case 2)
Ai Cloud Admin AI Cloud
Infrastructure
To ensure proper cloud
operations, only the cloud
administrator is allowed to
manage image offereings in
the cloud.
FY Students
In order to allow other
FY students to have
access to the new
application image for
their own project, the
originator of the
application has to work
with the cloud admin to
package the app as an
image offering in the
cloud.
Cloud Portal
Provisioning
manager packages
app image with OS
Image is registered
with service
automation
manager and portal
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
27
During 2 hr class,
provides VM login
information to 40
students in class /
exam
Use Case 4 : Lecturers prebooking seats for AI/ML/DL class or exam
AI Cloud Portal AI Cloud Infrastructure
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
lecturer via email
VMs deprovisioned
back into the cloud
Resources made available to
students for daily use will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
Lecturers
VM and Storage size :
40 VMs
2 core, 4GB RAM, 5GB
storage
RHEL
PowerAI Vision
Watson Machine Learning
Accelerator
Lecturer proceed to
request for VMs
with “Lecturer”
profile.
Select and request
for desired image,
and future usage
period eg. 40 VMs
of SPSS with LInux
for 2 hours.
Students access
VMs from laptop /
PC / workstations
Students download
work at end of class
Application and OS
images have to be
preconfigured by the
cloud admin before
use.
IP address for VM
deployed within same
subnet.
28
Use Case 5 : Researchers adding compute capacity with own
applications through the AI cloud
AI Cloud Portal Ai Cloud Infrastructure
Researchers
Researchers proceed
to request, access VM
and install own
application (as per
Use Case 2)
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
VM and Storage size :
8 cores, 16GB RAM, 250GB
storage
RHEL
Thank you!
Ganesan Narayanasamy
OpenPOWER global leader in Education &
Research
IBM Systems
29
Past years I have been enabling and engaging worldwide researchers , partners
and developers into our Disruptive Technologies and Open Technologies . What
excites me is receiving back insights on assignments/activities . According to me ,
One should never stop learning in life. The imagination of connected partners ,
researchers and students as they contemplate the applications and fusion of
emerging technologies in society and security is an inspiration.

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Special Purpose IBM Center of excellence lab

  • 2. Centers of Excellence for Innovation and Expertise IBM is teaming with universities, startups, ISV’s and industries to help develop Several Centers of Excellence for Innovations and developing expertise for real-world opportunities AI & HPC Center of Excellence Data Science Center of Excellence Industry 4.0 Center of Excellence Quantum Computing Center of Excellence
  • 3. 3 Background and Motivation The IBM CoE Labs will play a major role in the research and development commercial and industrial development of emerging technologies There is a strong need for research and development activity in these domains: – Encouraging academic-industry partnerships – Cross-disciplinary and collaborative research – Making technology accessible to non-technical business students – Enabling faculty-technologist interaction and learning – Enabling startups ,ISVs and industries to use the platform to innovate in ways that improve the World condition .
  • 5. 2 Year Developmental Timeline a) IBM Academic Initiative partnership b) OpenPOWER/IBM Servers and Accelerator for Solutions Developments c) Technical Projects deployment d) Review of progress in technical projects, lab coursework e) Workshops/Webinars
  • 6. Advantages for Faculty & Students Talent and Skills: (Remote Interns; Skills and Training) Students and Research scholars will start working on the advanced technologies will enable them to work on many applications Publications and Mindshare: (Press releases, Articles, and Publications; Conferences and Events) 1. Conference Paper on software-based application research /development in 6 months Intellectual Capital: (Patents, Open source; Prototypes, Demos; Curriculum; Student projects, Theses) 1. Prototype building of many research problems using software-centric approach (hardware-centric baseline implementation almost getting completed) 2. Potential to file disclosures Opportunities: (Seed revenue; Leverage other funding; Build ecosystems; Build government/client relationships) 1. Once software-centric solution available with comparable performance using latest technologies , your team would create prototypes which can be demonstrated to several colleges
  • 7. Our team of collaborators WE STRIVE FOR YOUR EXCELLENCE Condensed matter physicist and quantum technologist Scientific Program Manager for HPC/Supercomputing at NVIDIA Professor , The Ohio State University Associate Professor at The National Institute Of Engineering, Mysore CEO & Co-Founder at Appentra. High Performance Computing Software Research Associate Professor and Director, Performance Research Laboratory, University of Oregon Executive IT Architect, IBM Systems JACK C WELLS Dhabaleswar K (DK) Panda Christopher Coleman SAMEER SHENDE CLARISSE HEDGLIN ABHINANDAN SP Manuel Arenaz Assistant Professor (Computer Science), University of Wisconsin-Platteville Arghya Kusum Das
  • 8. Comprehensive Training on Data & Data Modelling 8 MAJOR CONCEPTS & 32 MODULES DATA SCIENCES
  • 11. Comprehensive Training on Data & Data Modelling 8 MAJOR CONCEPTS & 5 CASE STUDIES AI TECHNOLOGIES
  • 16. Topics Covered Within Training Industry 4.0 Cyber Physical Systems (CPS) Big Data & Analytics in Industry 4.0 Smart Factory & Smart Products Additive Manufacturing Computational Vision • Introduction to Industry 4.0 • Enablers of Industry 4.0 • Scope of Industry 4.0 • Examples of Applications of Industry 4.0 • Realtime requirements for Application of Industry 4.0 in the Global Scenario • What are Cyber Physical Systems (CPS) • What are the use of CPS in Industry 4.0 • Examples of Cyber Physical Systems • One used case for Discussion • One Used case for development/implementing (Optional) • Introduction to Data Science • Python for Data Science and Data Analysis • Data Visualization (Optional Softwares) • Introduction to Machine Learning • Introduction to Deep Learning • Project (Optional) • What is a Smart Factory • What are Smart Products • How are Smart Products related to Smart Factories • Advantages of Smart Products • Industry 4.0 and Smart Factories • Use Cases and Discussion Project (Optional) • Additive Manufacturing(AM) Concepts • Incorporation of AM in Industry 4.0 • Hands-On project on AM (Optional) • Application based solution through AM • Use cases from Global Applications • Project (Optional) • Introduction to Computational Vision • Programming for Computational Vision • Practical Project for Implementation • Suggestions/Support for Final Year research scopes (Optional and as required) • Support for Advanced Research • Designing & Programming of the Web of Things • AI and Machine Learning for IoT Big Data • Case Study Implementationsfor Industry 4.0 • Introduction to the Internet of Things • Design & Functioning of Wireless IoT Technologies (softwares introduced) • Sensors & Technologies used for IoT • Global IoT Use Cases Internet of Things
  • 17. Ideal place to connect, learn more and track trends AN IDEAL PLACE TO CONNECT WITH QUANTUM COMPUTING WORLD QUANTUM HUB
  • 18. Highlights of Quantum Hub Regular online workshops and information sessions featuring panel discussions will experts in Quantum Computing. All the latest news on technology developments including new quantum platforms, breakthroughs on existing systems as well as use cases and real- life applications. Access to our database of market analytics including our quarterly report on emergent and established players. Ø Ø Ø
  • 19. 19 Proposed AI cloud setup and specifications - Hardware College Ethernet Network 4 4 College Ethernet Network 2 IBM AC922 System 128 GB Memory 2 TB Hard drive 40 Cores Power 9 Processor NVLINK-2 nVidia GPUs – 8 1 IC922 System with 2 T4 GPUs 1 Login Server applications 1 EDR Infiniband swtich 16 TB Storage Sub System IB ConnectX cards Edge Compute devices 1 RACK ( Which can fit in )
  • 20. 20
  • 21. The AC922 has 2 POWER9 sockets, each providing extreme levels of IO and memory bandwidth. As an example, in the configuration proposed, each socket will communicate with two Nvidia V100 GPUs directly utilizing the 300GB per second NVLink2 bus connections on each POWER9 socket. In addition, sockets have high memory bandwidth, PCIe Gen4 bandwidth, and a high bandwidth SMP interconnect. 21
  • 22. 22 AI Lab users AI Lab Software Components
  • 23. University Use Cases and Scenarios of Proposed AI Lab AI Cloud at Universities
  • 24. 24 Use Case 1 : Students (daily use) requests for compute resource Basic ML/DL exercises Login to web portal with “Student” profile; browse service catalog. Select and request for desired image, and usage period eg. MS Office with Windows for 2 hours. Login and access Docker Container (Remote Desktop) AI Cloud Portal AI Cloud Infrastructure User / profile authentication Service request processing & approval VM & storage created according to request OS deployed into Docker Container Application image deployed into Docker Container Login info sent to user via email Docker Container with PowerAI image f Downloads completed work into laptop and logs off. Resources made available to students for daily use will be restricted. The restriction will be enforced through profile management on the cloud portal. Students Students login from anywhere within the UM LAN. Cloud portal is accessed via a web browser. Application and OS images have to be preconfigured by the cloud admin before use.
  • 25. 25 Use Case 2 : Final Year Students requests for compute resource for AI Projects Login to webportal with “FY” profile; browse service catalog. Select and request for desired image, and usage period e Login and access Docker Container (Remote Desktop) AI Cloud Portal Cloud Infrastructure User / profile authentication Service request processing & approval VM & storage created according to request OS deployed into VM Application image deployed into VM Login info sent to user via email VM deprovisioned back into the cloud Downloads completed work into laptop and logs off. Resources made available to final year students will be restricted. The restriction will be enforced through profile management on the cloud portal. FY Students Students login from anywhere within the UM LAN. Cloud portal is accessed via a web browser. IP address for VM deployed within same subnet. Students access from laptop. VM and Storage size : 2-4 cores, 4GB RAM, 10GB storage RHEL Jetson Nano VM for the FY student will be operational until the expiration date stated in his request.
  • 26. 26 Use Case 3 : Final Year Students creates own application image, and shares image with other FY students. Student to seek approval from Cloud Admin to create new app image in cloud infra New image is displayed in the service catalog Other FY students proceed to request, access and use new application (as per Use Case 2) Ai Cloud Admin AI Cloud Infrastructure To ensure proper cloud operations, only the cloud administrator is allowed to manage image offereings in the cloud. FY Students In order to allow other FY students to have access to the new application image for their own project, the originator of the application has to work with the cloud admin to package the app as an image offering in the cloud. Cloud Portal Provisioning manager packages app image with OS Image is registered with service automation manager and portal User / profile authentication Service request processing VM & storage created according to request OS deployed into VM Application image deployed into VM Login info sent to user via email VM deprovisioned back into the cloud
  • 27. 27 During 2 hr class, provides VM login information to 40 students in class / exam Use Case 4 : Lecturers prebooking seats for AI/ML/DL class or exam AI Cloud Portal AI Cloud Infrastructure User / profile authentication Service request processing VM & storage created according to request OS deployed into VM Application image deployed into VM Login info sent to lecturer via email VMs deprovisioned back into the cloud Resources made available to students for daily use will be restricted. The restriction will be enforced through profile management on the cloud portal. Lecturers VM and Storage size : 40 VMs 2 core, 4GB RAM, 5GB storage RHEL PowerAI Vision Watson Machine Learning Accelerator Lecturer proceed to request for VMs with “Lecturer” profile. Select and request for desired image, and future usage period eg. 40 VMs of SPSS with LInux for 2 hours. Students access VMs from laptop / PC / workstations Students download work at end of class Application and OS images have to be preconfigured by the cloud admin before use. IP address for VM deployed within same subnet.
  • 28. 28 Use Case 5 : Researchers adding compute capacity with own applications through the AI cloud AI Cloud Portal Ai Cloud Infrastructure Researchers Researchers proceed to request, access VM and install own application (as per Use Case 2) User / profile authentication Service request processing VM & storage created according to request OS deployed into VM Application image deployed into VM Login info sent to user via email VM deprovisioned back into the cloud VM and Storage size : 8 cores, 16GB RAM, 250GB storage RHEL
  • 29. Thank you! Ganesan Narayanasamy OpenPOWER global leader in Education & Research IBM Systems 29 Past years I have been enabling and engaging worldwide researchers , partners and developers into our Disruptive Technologies and Open Technologies . What excites me is receiving back insights on assignments/activities . According to me , One should never stop learning in life. The imagination of connected partners , researchers and students as they contemplate the applications and fusion of emerging technologies in society and security is an inspiration.