Project report on training in DRDO.

1. INDUSTRIAL TRAINING REPORT
Defence Research and Development
Organisation(DRDO)
Submitted in partial fulfilment of the
Requirements for the award of
Degree of Bachelor of Technology in Computer Science in
Computer Science & Engineering
Submitted By:
Name: Ankit
University Roll No.: 010002718
Department of Computer Science & Engineering
DELHI TECHNICAL CAMPUS, GREATER NOIDA
UTTAR PRADESH

2. Acknowledgement
I would like to express my sincere gratitude to my supervisors
Mr.Vivek Krishna Mishra for providing their invaluable guidance and
suggestions throughout the course of the project. I would also like
thank my friends for constantly motivating me to work.
I had made this project from my heart and shown utmost sincerity to
complete it. I am very thankful to all those people who helped me and
guided me to make such a project.

3. DECLARATION
I hereby declare that the Industrial Training Report entitled AI in
Semiconductors" is an authentic record of my own work as requirements of
Industrial Training during the period from October,1 2021 to November,30
2021 for the award of degree of B.Tech. (Computer Science &
Engineering), Delhi Technical Campus, Greater Noida, under the guidance
of Scientist Kamal Lohani
Date: 22 December, 2021 Ankit Kumar
01018002718

4. Certificate from Organisation

5. INDEX
 Artificial Intelligence in semiconductors
 How will AI affect semiconductor design and production?
 AI strategy for Semiconductor devices
 AI and the semiconductor market
 How AI change the demand for semiconductor chips?
 AI technology provides opportunities for semiconductor industries
 Impact of artificial intelligence on the semiconductor industry
 How AI technology affect semiconductor production?
 How will AI technology affect the workforce in the semiconductor
industry?
 Future of semiconductors and artificial intelligence
 How is Artificial intelligence expected to affect the semiconductor
industry in the future?
 Popularity of artificial intelligence in the semiconductor industry
 The challenges ahead for semiconductors AI chips
 Semiconductor industries benefit from AI technology
 The results of implementing AI in Semiconductors

6. Introduction to AI in semiconductors
• Artificial intelligence (AI) chips are comprehensive silicon chips which
integrate AI technology and are used for machine learning. AI helps to
eliminate or minimize the risk to human life in many industry shafts. the
need for more productive systems to solve mathematical and
computational problems is becoming critical, owing to the increase the
volume of data. thus, on developing ai chips and applications, a large
number of the key players in the it industry have dedicated themself.
moreover, the arrival of quantum computing and increased
implementation of AI chips in robotics steer the growth of the global
artificial intelligence chip market. in addition, the arrival of autonomous
robotics (robots that develop and control themselves independently) is
anticipated to provide potential growth opportunities for the market. till
recent years, most of the computations of AI are almost been done
distantly in data centres or on firm core appliances or on telecom edge
processors (not internally on devices).This is because AI computations are
requiring hundreds of varying types of chips to execute and are
significantly processor-intensive. it is fundamentally incredible to
integrate AI computations in anything smaller than a footlocker because
of its size; cost and power drain of the hardware. presently, all those have
been changed by ai chips. these AI chips are completely small, fairly
inexpensive, use less power and generate very less heat. these
parameters are making ai chips possible to integrate into handheld
devices such as smartphones and even into non-consumer devices such
as robots. therefore, ai chips can deliver the data with high speed,
security and privacy by allowing the above devices to execute processor-
intensive ai computations locally thereby reducing or eliminating the
necessity to send large amount of data to a remote location

7. Today, semiconductors are important technology enablers that
power many of the cutting-edge digital devices. The global
semiconductor industries are assigned to maintain its robust growth
due to arriving technologies such as autonomous driving, artificial
intelligence (AI), 5G and Internet of Things in the following decade.
Many budding divisions especially in the automotive sector and AI
will provide huge opportunities for semiconductor companies. AI
semiconductor has seen a sprint not just at the application level but
also at the semiconductor chip level, commonly known as AI Chips.
As the term suggests, AI chips refers to a recent generation of
microprocessors which are particularly designed to process artificial
intelligence tasks faster, using less power. AI chips could play a
crucial function in economic growth moving forward because they
will surely feature in cars which are becoming deliberately
autonomous, smart homes where electronic devices are becoming
more intelligent, robotics and many other technologies

8. How will AI affect semiconductor design and production?
AI demands will have lasting impacts on semiconductor design and
production. In large part, this is because the amount of data processed
and stored by AI applications is massive.
Semiconductor architectural improvements are needed to address data
use in AI-integrated circuits. Improvements in semiconductor design for
AI will be less about improving overall performance and more about
speeding the movement of data in and out of memory with increased
power and more efficient memory systems.
One option is the design of chips for AI neural networks that perform
like human brain synapses. Instead of sending constant signals, such
chips would “fire” and send data only when needed.
Nonvolatile memory may also see more use in AI-related
semiconductor designs. Nonvolatile memory can hold saved data
without power. Combining nonvolatile memory on chips with
processing logic would make “system on a chip” processors possible,
which could meet the demands of AI algorithms.
While semiconductor design improvements are emerging to meet the
data demands of AI applications, they pose potential production
challenges. As a result of memory needs, AI chips today are quite large.
With this large chip size, it is not economically easy for a chip vendor to
make money while working on a specialized hardware. This is because it
is very costly to manufacture a specialized AI chip for every application.
A general-purpose AI platform would help address this challenge.
System and chip vendors would still be able to augment the general-
purpose platform with accelerators, sensors, and inputs/outputs. This
would allow manufacturers to customize the platform for the different

9. workload requirements of any application while also saving on costs. An
additional benefit of a general-purpose AI platform is that it can facilitate
faster evolution of an application ecosystem.
From a production standpoint, the semiconductor industry will also
itself benefit from AI adoption. AI will be present at all process points,
proving the data needed to reduce material losses, improve production
efficiency, and reduce production times.
AI strategy for Semiconductor devices
The semiconductor market has, for most of the last decade, seen much
of its profits tied to the smartphone and mobile device market. As the
smartphone market begins to plateau, the semiconductor industry must
find other growth opportunities.
AI applications, especially in the big data, autonomous vehicles, and
industrial robotics industries, can provide those opportunities. By
defining and then putting together their AI strategies now,
semiconductor manufacturers can position themselves to take full
advantage of the spreading AI market.
AI and the semiconductor market
AI offers semiconductor companies the chance to get the most value
from the technology stack, the collection of hardware and services used
to run applications. In the software-dependent world of PCs and mobile
devices, the semiconductor industry is only able to capture 20 to 30
percent of the total value of the PC stack and as little as 10 to 20 percent
of the mobile market.
Within the AI sector, the technology stack requires more hardware,
especially in the fields of memory and sensors. This may allow the

10. semiconductor market to control 40 to 50 percent of the total value of
the stack, according to the Redline Group.
In addition, many AI applications will require specialized end-to-end
solutions, which will necessitate changes to the semiconductor supply
chain. Semiconductor companies—especially smaller companies
producing niche products for the automotive and IoT industries—will
be able to capitalize on markets by providing customized microvertical
solutions addressing customer pain points related to storage, memory,
and specialized computing needs.
How AI change the demand for semiconductor chips?
The global AI market is forecast to grow to $390.9 billion by 2025,
representing a compound annual growth rate of 55.6 percent over that
short period. Hardware lies at the foundation of each AI application.
Storage will see the highest growth, but the semiconductor industry will
reap the most profit by supplying computing, memory, and networking
solutions. Demand for semiconductor chips will mirror the rapid ascent
of the AI market.

11. AI technology provides opportunities for semiconductor industries
AI embedded chips (chips designed to work with neural networks and
machine learning) will see a growth rate of approximately 18 percent
annually—five times greater than that seen for semiconductors used in
non-AI applications. Areas of high growth will include AI chips for
autonomous vehicles and in the broader field of neural networks.
Neural networks are specialized AI algorithms based on the human
brain. The networks are capable of interpreting sensory data and
delivering patterns in large amounts of unstructured data. Neural
networks find use in predictive analysis, facial recognition, targeted
marketing, and self-driving cars. And they require AI accelerators and
multiple inferencing chips, all of which the semiconductor industry will
supply.
Impact of artificial intelligence on the semiconductor industry
The immediate future of AI has the potential to put strain on the
industry supply chain unless semiconductor manufacturers plan to
meet demand now. At the same time, the industry will itself benefit
from AI, whose applications throughout the manufacturing process will
improve efficiency while cutting costs.
How AI technology affect semiconductor production?
Just as other industries are embracing AI, so too is the semiconductor
industry . AI expertise coupled with high-performance computing will
allow manufacturers to develop new efficiency benchmarks and
increase output.
One of the key challenges to the semiconductor supply chain is chip
production processing time. The time between initial processing and the
final product takes weeks. And during this time, up to 30 percent of
production costs is lost to testing and yield losses.
Embedding AI applications into the production cycle allows companies

12. to systematically analyze losses at every stage of production so
manufacturers can optimize operating processes. This ability will
become even more valuable when working with next-generation
semiconductor materials, which tend to be more expensive (and
volatile) than traditional silicon.
How will AI technology affect the workforce in the semiconductor
industry?
While the rise of AI brings many opportunities to the semiconductor
industry, it also heralds a crisis in talent acquisition. The larger tech
companies—most notably Google, Apple, Facebook, Amazon, and the
like—are investing heavily in AI research, development, and
implementation, especially in the arenas of big data analytics and deep
learning.
This represents two challenges to chip makers. First, the major players
in the AI industry increasingly develop their own hardware as this
allows them to customize proprietary hardware to match their AI
applications’ specific needs. This move toward in-house chip
production, by extension, means the largest tech companies will
purchase less from dedicated chip manufacturers.
Second—and this is where workforce considerations come into play—
tech giants designing and manufacturing their own chips in house will
need employees. With limited talent pools in both AI and the
semiconductor industry, this will lead to talent shortages.

13. Law, semiconductor research and development will need to consider
how sensors, memory, and microprocessors enable and support
emerging AI applications. Focusing on serving the needs of AI and the
equally important IoT industry will help keep chip makers at the
forefront of the industry.
Popularity of artificial intelligence in the semiconductor industry
Demand from both the public and private sectors is driving the rapid
development of AI—and as a result the importance of AI to the
semiconductor industry. Of special note is the trend toward advanced
driver assistance systems and electric vehicles. Even if the arrival of
truly autonomous vehicles in large numbers remains years away,
automotive AI applications for monitoring engine performance,
mileage, and driver habits are already here. Insurance companies are
already using in-car AI apps to evaluate driving habits and determine
premium rates.
While the smartphone industry is plateauing in terms of growth, the
demand for embedded AI in mobile devices is growing. Phones use AI
for navigation, for voice-to-text software, for facial recognition security,
and for personal assistants. The advent of Alexa and other smart home
hubs—and their ability to be controlled from afar by phone apps—
represents another growth area for AI.
Then there are the uses for AI the general public is only tangentially
aware of. City planners increasingly rely on AI to report on traffic
volume, sewer usage, and infrastructure maintenance. Utility
companies use AI to set electricity and water rates or to alert
technicians to incidents or maintenance events.

14. Future of semiconductors and artificial intelligence
Self-driving cars. High-performance computing. Quantum computing. AI
makes what was science fiction at the turn of the century into reality.
With these AI advances come demands for new semiconductor
technology and deep changes to the industry.
How is Artificial intelligence expected to affect the semiconductor
industry in the future?
To adapt to an industry increasingly dominated by the need for AI
hardware, semiconductor manufacturers will need to provide industry-
specific end-to-end solutions, innovation, and the development of new
software ecosystems.
End-to-end services will require chip makers to work with partners to
develop industry-specific AI hardware. While this may limit the
semiconductor manufacturer to working with only certain industries, the
alternative—the traditional production of general products—may not
attract the same customers it does at present. An exception would be
the production of cross-industry solutions that serve the needs of an
interrelated group of industries.
With the production of specialized products comes the need to develop
existing ecosystems with partners and software developers. The goal of
such ecosystems is to develop relationships in which partners rely on
and prefer the semiconductor company’s hardware. Semiconductor
manufacturers will need to produce hardware that partners cannot find
elsewhere at similar value. Such hardware—coupled with simple
interfaces, dev kits, and excellent technical support—will help build long-
lasting relationships with AI developers.
Innovation, as always, plays a role in the future of semiconductors. In
addition to the ongoing efforts to circumvent the limitations of Moore’s

15. Retail and online retail stores use AI to predict consumer needs and
preferences—with what some see as alarming precision. Similar
software is used by major social network platforms when choosing
content and ads for individual users. AI has applications in health care,
bioscience, industry, government, and the military—anywhere where
large amounts of data need to be processed quickly, analyzed, and
acted upon.
The challenges ahead for semiconductors chips
The majority of the challenges the AISemiConductors face are
still the same old problems faced by general-purpose CPU and
GPU as the technology at the silicon level advanced. The new
AISoC solution from both the established companies and
startups are eventually going to hit with these challenges.
Cost: Designing and establishing AISoC proof-of-concept using
the software simulator demands resource and pushes the cost
of development from FAB to OSAT. The cost of owning
smartphones and running data centers is already high. On top
of it, any new solution with AI-power will add cost to the
customer. The technology node required to enable a high
number of processing units to speed up the training and
inference is eventually going to cost money. AISoC vendors

16. need to balance the cost of manufacturing in order to
breakeven the market. On top of all this, the amount of
competition in developing new AISoC means time to market is
vital than ever.
Performance: The reason to move away from general-purpose
CPU and GPU was memory and interconnect bottleneck. There
are few startups listed above that are trying to remove these
bottlenecks. However, with the speed with which new AI-
workload are getting generated, there is a high chance that
bottlenecks will still exist. It will be vital to ensure that the new
type of AISoC that both the established companies and startups
are envisioning does not have any bottlenecks.
Bandwidth: Bringing the data closer to the processing units
(any type) is the key to processing AI data faster. However, for
such a task high-speed memory with large bandwidth is
required. The new AISoC are incorporating new processing
units , and so on, but there is no clear strategy and details on
how the data communication bandwidth is improved. May be
such details are proprietary.
Programming: In the end, any AISoC cannot process the data
efficiently if the workload is not optimized for the target
architecture. While few AISoC is pitching their products as no
need to change the data or framework before running it on
their architecture, however, the reality is that every
architecture ends up needing some or other form of
optimization. All this adds to the time to develop data
solutions.

17. Manufacturing: As the new AISoCs come out in the market,
many of these will end up using advanced nodes beyond 7nm
to provide high speed. Advanced packaging technology also is
required to operate the AISoC within the thermal budget. Both
the complex technology node and package technology will
drive a high manufacturing cost. Apart from this, balancing
yield and cost will be essential to ensure AISoC development is
viable.
Power Consumption: AISoC requires zillions of transistors that
require faster cooling. The majority of the AISoC can do with
liquid cooling but when such AISoC is connected together to
form data centers then the cost to run data centers goes high.
Hopefully, greener technologies will be able to run such data
centers. However, the AISoC will get challenged to overcome
the area, power, and thermal wall.
No matter what, AISoC in coming years is going to be the
semiconductor domain that will innovate and provide elegant
semiconductor solutions that will challenge the end-to-
ensemiconductor design and manufacturing.

18. Semiconductor industries benefit from AI technology
AI adoption holds the possibility for growth in the following areas of
semiconductor manufacturing:
 Workload-specific AI accelerators
 Nonvolatile memory
 High-speed interconnected hardware
 High-bandwidth memory
 On-chip memory
 Storage
 Networking chips
 Investing in research and development while building
relationships with AI software providers will help chip
manufacturers capture their share of these markets—if they can
meet the coming demand

19. The results of implementing AI in Semiconductors
The impact of using AI in the semiconductor industry has been huge.
Overall, yield detraction has been reduced by up to 30 percent, and the
cost reduction benefits of using AI-based algorithms for testing cover a
variety of areas:
 The ability of AI to identify root causes can reduce scrap rates which
improves yield.
 AI can improve the overall effectiveness of equipment by lessening the
requirement for equipment and maintenance.
 The cost of test procedures is reduced when they are AI-optimized.
 A reduction or stabilization in the flow factor can lead to higher
throughput.