Object Automation, a California-based technology company, proposes a collaboration with a university to establish an AI Center of Excellence focused on developing research and solutions in AI, quantum computing, and chip design. The initiative aims to optimize processes across organizations by leveraging artificial intelligence, providing cloud infrastructure for students and faculty, and incorporating workshops and technology partnerships to enhance learning and innovation. Key use cases include web portal access for students and researchers to request computational resources and work on projects, ensuring tailored support for different academic needs.

1. Proposed Collaboration with
your University
Center of Excellence using AI

2. Who are we
Object Automation, a technology company based in California,
has been concentrating on latest technologies and emerging
tech partnerships. These include research and solution
development, the development of onshore and offshore
technology projects, the establishment of tech centers of
excellence in AI, quantum, and chip design,, technology
workshops and boot camps for corporates, special labs for
universities, and cutting-edge industry projects.
With a development center in India and numerous global
partners, the company works together to provide AI and chip
design solutions globally.

3. Object Automation, USA is leading the way
Teaming with universities, startups, ISV’s and
industries to help develop further the impact
of artificial intelligence for solutions for real-
world opportunities

4. Technologies
and Partners
The AI Lab will include IBM
and other corporate
sponsors, coupled with
open-source technologies to
accelerate results
3

5. 4
Why AI Center of Excellence ?
Artificial intelligence is one of the most powerful technologies for reshaping
business in decades. It can optimize many processes throughout
organizations and is already the engine behind some of the world’s most
valuable platform businesses. In our view AI will become a permanent aspect
of the business landscape and AI capabilities need to be sustainable over time
in order to develop and support potential new business models and
capabilities.
Create a vision for AI in the University. It’s important for executives to
discuss — ideally with AI experts — what AI is, what it can do, and how it
might enable new business models and strategies.
Manage external innovation
Develop and maintain a network of AI champions
Acquire and Build Talent
Spread success stories. A key success factor with AI or any new
technology is to spread early success stories with prioritized use cases.
This will build the appetite for more AI activity; in effect such
communications perform a marketing function for the AI center.

6. IBM Power Explained - Torbjörn Appehl
OpenPOWER Foundation

7. IBM’s AI Lab
Special System for Data
Analytics with
Accelerators (GPU)
Collaborative technical projects
Access to Academic Initiative
Toolkit
Graduate, Ph.D. and Post-Doctoral
research
Webinars and Technical Workshops
Projects related to make smart cities
and smart villages

8. University Use Cases and Scenarios of
Proposed AI Lab
AI Cloud at Universities

9. 19
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)
Students 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 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.

10. 20
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)
FY Students 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.
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.

11. 21
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)
FY Students Ai Cloud Admin AI Cloud
Infrastructure
To ensure proper cloud
operations, only the cloud
administrator is allowed to
manage image offereings in
the cloud.
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

12. 22
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.

13. 23
Use Case 5 : Researchers adding compute capacity with own
applications through the AI cloud
Researchers AI Cloud Portal Ai Cloud Infrastructure
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

14. 2 Year Developmental Timeline
a) Academic Initiative partnership
b) Accelerator for Deep Learning
and Machine Learning
c) Technical Projects deployment
d) Review of progress in technical
projects, lab coursework
e) Big data and AI curriculums

15. HPC/AI Scheduler
27

16. Solution for HPC and AI
28

17. AI Cloud (On
Premise)
PowerAI makes deep
learning, machine learning
and AI more accessible and
more performant
By combining this software
platform for deep learning with
IBM Power Systems,
enterprises and Institutions can
rapidly deploy a fully optimized
and supported platform for
machine learning frameworks
and their dependencies. And it
is built for easy and rapid
deployment
PowerAI runs on the IBM Power
System AC922 for High
Performance Computer server
infrastructure

18. Advantages for Your Faculty and
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

19. Special Courses
§ Machine Learning with Python
§ Enterprise AI
§ Applied Gen AI
§ Quantum Computing
§ Cyber Security with AI
§ Azure Integrated Machine Learning
§ Faculty Development programs
§ Chip Design
More than 100 hours of technology workshops/Courses

20. Processor Core Enablement and
Partnership
1. Introduction of open-ended experiments on A2I
Core in the FPGA Lab curriculum
2. Allotment of Mini Projects to students on
HDL/Verilog/ A2I Core
3. Global Remote Mentoring for students with our
mentors, who have desired FPGA coding skills
4. FDP for faculty on porting & integration of modules
for application design using A2I core
5. Discussion on the creation of data-path for the
development of softcore processor architecture
6. Joint research activities
7. Development of specific solutions for IBM as
sponsored projects / consultancy
8. Sharing of learning materials for A2I core and
relevant tool chain
32

21. University of Oregon , E4S and TAU
Collaborations
E4S or the Extreme-scale Scientific Software Stack [https://e4s.io] is a community effort to provide open-
source software packages for developing, deploying and running scientific applications on high-
performance computing (HPC) platforms. E4S provides from-source builds and containers of a broad
collection of HPC software packages. E4S exists to accelerate the development, deployment and use of
HPC software, lowering the barriers for HPC users.
§ "TAU Performance System® [http://tau.uoregon.edu] available on
OpenPOWER:
– Profiling and tracing support with 3D profile browsers
– Support for IBM XL, GNU, and LLVM Clang compilers
– Support for PowerAI, Spectrum MPI, and MVAPICH2 GDR, CUDA,
OpenACC
– Multi-platform support in TAU
• IBM Power, Cray XC, ARM64, x86_64, NVIDIA CUPTI and AMD
GPUs (ROCm)
34

22. Ohio State University and xScale
solutions collaborations
35

23. Reach US