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1. 1
ANURAG COLLEGE OF ENGINEERING
Aushapur(V), Ghatkesar(M), Medchal(D),T.S
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
NEUROMORPHIC COMPUTING
Under the guidance of :-Mr S.VIJAY KUMAR
Assistant Professor.
Presented by:
Mr. PRUTHVY NAIDU KSV
19PQ1A0565

2. CONTENTS
1. Abstract
2. Introduction
3. Existing System
4. Proposed System
5. Construction & Working
6. Applications
7. Advantages
8. Challenges
9. Future Scope
10. Conclusion
11. Bibliography
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3. ABSTRACT
The development of novel functional materials and devices incorporated into
unique architectures will allow a revolutionary technological leap toward the
implementation of a fully “neuromorphic” computer.
Compared with von Neumann's computer architecture, neuromorphic systems
offer more unique and novel solutions to the artificial intelligence discipline.
This paper presents a comprehensive review and focuses extensively on the
Hopfield algorithm's model and its potential advancement in new research
applications.
Towards the end, we conclude with a broad discussion and a viable plan for the
latest application prospects to facilitate developers with a better understanding of
the aforementioned model in accordance to build their own artificial intelligence
projects.
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4. INTRODUCTION
 A neuromorphic computer is not a brain, although if we were ever to figure out
how to simulate a brain on a computer, a neuromorphic computer would likely be
an efficient option.
 Neuromorphic chips which powers neuromorphic computers may not replace
conventional computational chips such as CPU GPU or application-specific ICs.
 A neuromorphic computer will be more / less efficient than another computing
architecture depending on the algorithm. A key question in designing a
neuromorphic computer is understanding the structure of the algorithms it will
likely run.
 However neuromorphic computers have ability to add to existing computers that
performs deep learning for artificial intelligence.
 Neuromorphic chips which powers neuromorphic computers may not replace
conventional computational chips such as CPU GPU or application-specific ICs.
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5. EXISTING SYSTEM
Separate processing and memory units.
 Von Neumann computers have separate central processing units for processing
data and memory units for storing data.
 Von Neumann computers encode data using binary values. Speed and energy
issues. To compute, data must be moved between the separate processing and
memory locations. This approach is known as the von Neumann bottleneck; it
limits a computer's speed and increases its energy consumption.
 Neural network and machine learning software run on von Neumann hardware
typically must provide either fast computation or low energy consumption,
achieving one at the expense of the other.
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6. PROPOSED SYSTEM
1. Collocated Processing and memory
2. Massively Parallel
3. Inherently Scalable
4. Event-Driven Computation
5. High in adaptability and plasticity
6. Fault Tolerance
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7. 7
ARCHITECTURE

8. WORKING
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• Modelled on biological brains—
designed to process sensory data
such as images and sound and
respond to changes in that data in
ways not specifically
programmed.
• Neuromorphic computing systems
excel at computing complex
dynamics using a small set of
computational primitives
(neurons, synapses, spikes).

9. APPLICATIONS
 Medicine
 Large Scale Operations & Product Customization
 Artificial Intelligence
Imaging.
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10. ADVANTAGES
•Can compute in real time, which is similar to how
the brain works
•Might take us a step closer to artificial intelligence
•Different applications in various disciplines
•Advances in neuroscience and chip making
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11. CHALLENGES
1. Accuracy
2. Limited software and algorithms
3. Inaccessible
4. Neuroscience
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12. FUTURE SCOPE
 Forecasts vary, but enormous growth seems likely. The current
neuromorphic computing market is majorly driven by increasing demand
for AI and brain chips to be used in cognitive and brain robots. These
robots can respond like a human brain.
 Numerous advanced embedded system providers are developing these
brain chips with the help of AI and machine learning (ML) that acts as
thinks and responds as the human brain.
 This increased demand for neuromorphic chips and software for signal,
data, and image processing in automotive, electronics, and robotics
verticals is projected to further fuel the market.
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13. CONCLUSION
 Neuromorphic systems offer two major promises. First, because they are
pulse-driven, potentially asynchronous, and highly parallel, they could be a
gateway to an entirely new way of computing at high performance and very
low energy.
 Second, they could be the best vehicle to support unsupervised learning a
goal that may prove necessary for key applications such as autonomous
vehicle navigation in unchartered areas or natural-language comprehension.
 Therefore, with neuromorphic chips the system learns along the way, much
like how humans learn. This is unlike the brute force deep-learning procedure
which focuses on interpreting large and varied sets of data in order to train a
conventional computer to make predictions.
 A new generation of personal assistants might emerge, augmenting humans
in learning tasks, a development with far-reaching consequences for our
economic and social environment.
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14. BIBLIOGRAPHY
https://www.foreignaffairs.com/articles/2015-08-12/moral-code
http://www.scientificamerican.com/article/the-many-ethical-implications-of-emerging-
technologies/
https://wtvox.com/robotics/top-5-emerging-technologies-in-2015/
http://www.popsci.com/ibms-neuromorphic-chip-array-mimics-rodent-brain
http://www.technologyreview.com/featuredstory/526506/neuromorphic- chips/
http://techcrunch.com/2015/01/31/the-ongoing-quest-for-the-brain-chip/
http://www.eetimes.com/author.asp?section_id=36&doc_id=1326549&page_number=2
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15. 15
QURIES ?

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