The Basics of Neuromorphic Computing

Summary

Orange Banerjee's seminar presentation delves into neuromorphic computing, a technology mimicking the human brain's efficiency. Discussing its origins from the von Neumann architecture to the current advancements, Orange highlights the potential of neuromorphic systems in AI and space missions. He introduces key concepts like memristors and compares neuromorphic chips like IBM's TrueNorth and Intel's Loihi, emphasizing their energy efficiency and processing power. Challenges in designing and programming these systems are also addressed, questioning if our current understanding of the brain is sufficient to fully harness neuromorphic computing's potential.

Takeaways

• 🎓 Orange Banerjee, a student at Bennett University, is presenting on the topic of neuromorphic computing.
• 🧠 Neuromorphic computing aims to create hardware that mimics the neurobiological architectures present in the human nervous system.
• 💡 The concept of neuromorphic computing was invented by Carver Mead in the 1980s, focusing on VLSI systems to replicate brain-like functions.
• 🚀 The von Neumann architecture, which separates memory and CPU, is a bottleneck for AI and machine learning advancements compared to the brain's integrated approach.
• 🔋 Neuromorphic systems are more energy-efficient compared to traditional computing, which is crucial as technology advances and energy demands increase.
• 🌐 Moore's Law predicts the exponential growth of transistors on microchips, but this growth could lead to an unsustainable energy consumption for von Neumann architectures.
• 🤖 Neuromorphic computing holds promise for AI by potentially making supercomputers faster and enabling space operations with adaptable, learning systems.
• 🔗 IBM's TrueNorth and Intel's Loihi are examples of neuromorphic chips that have been developed to process information more efficiently.
• 🛠 The design and analysis of neuromorphic systems present challenges, including the need for new programming languages and hardware innovations.
• 🤔 There are still many unknowns in neuromorphic computing, such as the replication of human emotions and the full complexity of the brain's functions.

Q & A

What is the main topic of Orange Banerjee's seminar presentation?

The main topic of Orange Banerjee's seminar presentation is neuromorphic computing.

Which university is Orange Banerjee pursuing his V-Tech in CSE from?

Orange Banerjee is pursuing his V-Tech in CSE from Bennett University.

What is the von Neumann architecture and why is it significant in the context of neuromorphic computing?

The von Neumann architecture is a computer architecture where the CPU and memory are separate, which leads to a bottleneck in data transfer. It's significant in neuromorphic computing because it contrasts with the human brain's architecture, which is more efficient and does not have such a bottleneck.

Who invented neuromorphic computing and what is its main principle?

Neuromorphic computing was invented by Carver Mead in the 1980s. Its main principle is to create integrated circuits that replicate or mimic the neurobiological architecture present in the human nervous system.

What is Moore's Law and how does it relate to neuromorphic computing?

Moore's Law states that the number of transistors on a microchip doubles every two years, and the cost of computers is halved. It relates to neuromorphic computing as it highlights the exponential growth of technology, which is challenged by the energy inefficiency of traditional architectures, making the energy-efficient neuromorphic systems more appealing.

Why is there a need for neuromorphic systems according to the presentation?

There is a need for neuromorphic systems because traditional von Neumann architecture is energy-hungry and faces limitations in processing power and efficiency, especially when compared to the human brain's capabilities.

What is a memristor and how does it relate to neuromorphic computing?

A memristor is an electrical device that remembers the amount of current or voltage that has passed through it. It is crucial in neuromorphic computing because it can mimic the synaptic behavior found in the human brain, allowing for the creation of artificial synapses.

What is the potential impact of neuromorphic computing on space operations?

Neuromorphic computing can make space missions more efficient by reducing the need for ground mission teams, as it allows space vehicles to adapt and learn according to their environment, thus requiring less power and processing capabilities.

What are some of the challenges faced in developing neuromorphic systems?

Challenges in developing neuromorphic systems include designing and analyzing the structure, creating new programming languages, and developing new generations of memory storage and sensor technologies.

What are the two neuromorphic systems mentioned in the presentation, and what are their key differences?

The two neuromorphic systems mentioned are the Neural Grid and IBM's TrueNorth. The Neural Grid uses sub-threshold analog logic and is smaller in scale, while TrueNorth is larger, more efficient, and uses digital memristor devices instead of traditional VLSI systems.

How does the efficiency of the human brain compare to modern supercomputers in terms of processing power?

The human brain is significantly more efficient than modern supercomputers. It can process around 10^18 floating points per second on just 20 watts of power, making it five times faster than the world's largest supercomputer, which is IBM's Summit.

Outlines

00:00

🧠 Introduction to Neuromorphic Computing

Orange Banerjee, a student at Bennett University pursuing a degree in CSE, introduces the topic of neuromorphic computing in a seminar presentation. He expresses his interest in AI and machine learning and acknowledges the support from his university and faculty. The presentation delves into the history of computing, highlighting the von Neumann architecture as the foundation of modern computer systems. Neuromorphic computing is presented as a revolutionary approach that mimics the neurobiological architecture of the human nervous system using VLSI systems. The speaker emphasizes the need to understand the brain's workings to advance in this field and discusses the limitations of the von Neumann architecture, such as the separation of CPU and memory, which contrasts with the brain's integrated processing and memory. The presentation also touches on Moore's Law and its implications for the future of computing, particularly the energy demands that could outstrip global energy budgets.

05:02

🔋 Energy Efficiency and Neuromorphic Systems

The second paragraph discusses the inherent latency and bottleneck in traditional computing architectures due to the separation of processing and memory units. It contrasts this with the brain's efficiency, where processing and memory are co-located, reducing latency. The human brain's remarkable energy efficiency is highlighted, noting that it operates on a mere 20 watts of power, which is comparable to the energy used by a cup of coffee. The speaker emphasizes the need for neuromorphic systems that can replicate the brain's efficiency and processing capabilities. An example of a neuron and an artificial neuron is used to illustrate the concept of data transfer through ions and the potential for creating artificial synapses using memristors, which have memory properties. The paragraph concludes with a call to explore the vast field of neuromorphic computing and the potential for creating systems that mimic the human brain's functionality.

10:02

🚀 Neuromorphic Computing for Space and AI

In the third paragraph, the speaker explores the potential applications of neuromorphic computing, particularly in space operations and artificial intelligence. Neuromorphic systems are highlighted for their adaptability and flexibility, which are currently unmatched by traditional AI systems. The potential for neuromorphic computing to make supercomputers faster and more efficient in space missions is discussed, along with the ability to reduce the need for ground mission teams. The speaker also touches on the importance of reducing data processing requirements and the potential for neuromorphic chips to revolutionize space exploration. The paragraph concludes with a discussion of the architecture of neuromorphic systems, emphasizing the need for memristors and the stacking of these components to create integrated circuits that can mimic the brain's structure and function.

15:05

💡 Neuromorphic Chips and Their Development

The fourth paragraph delves into the development of neuromorphic chips and their capabilities. The speaker mentions the progress made in the field, with companies like IBM and Intel investing in neuromorphic technology. IBM's TrueNorth chip, introduced in 2014, is highlighted for its 64 million neurons and 16 billion synapses, making it a significant milestone in neuromorphic computing. Intel's Loihi chip, introduced in 2020, is noted for its efficiency and potential to scale up to 100 million synapses. The speaker compares the processing power of neuromorphic systems to that of the human brain and supercomputers, emphasizing the potential for neuromorphic systems to outperform current technology. The paragraph also discusses the architecture of neuromorphic chips and the challenges of integrating them into existing systems.

20:07

🛠 Challenges and Future of Neuromorphic Computing

The final paragraph addresses the challenges and future prospects of neuromorphic computing. The speaker notes the difficulties in designing and analyzing neuromorphic systems, suggesting that new programming languages and hardware may be required. The limitations of current neuromorphic designs are discussed, particularly the lack of consideration for the full complexity of the human brain, such as glial cells and emotions. The speaker questions whether we have enough understanding of the brain to replicate its functions effectively. The paragraph concludes with a reflection on the potential of neuromorphic computing to revolutionize technology and the need for further research and development in this field.

Keywords

💡Neuromorphic Computing

Neuromorphic computing is a concept in computer science that involves designing and building computers or computer systems that mimic the neurobiological architectures present in the human nervous system. In the video, this term is central as it refers to the main topic of the presentation. The speaker discusses how neuromorphic computing aims to create solid-state devices that operate similarly to the human brain, which could revolutionize fields like artificial intelligence and machine learning.

💡Von Neumann Architecture

The Von Neumann architecture is a computer design model that separates the processor and memory, requiring data to be transferred between the two. This architecture is contrasted in the video with the more integrated approach of neuromorphic computing. The speaker points out that the Von Neumann bottleneck is a limitation for the development of advanced computing systems, as it does not efficiently replicate the parallel processing capabilities of the human brain.

💡Moore's Law

Moore's Law is the observation that the number of transistors on a microchip doubles approximately every two years, which historically has meant a corresponding increase in computing power. In the video, the speaker references Moore's Law to highlight the exponential growth of technology and the challenges it poses for energy consumption in traditional computing architectures, emphasizing the need for more energy-efficient computing models like neuromorphic computing.

💡Memristor

A memristor is an electrical component that can remember the amount of charge that has passed through it, effectively acting as a non-volatile memory element. In the context of the video, memristors are crucial for creating artificial synapses in neuromorphic computing, as they allow for the replication of the memory function of biological neurons. The speaker mentions that many advancements in neuromorphic computing, such as those by HP and IBM, are based on memristor technology.

💡Artificial Neural Networks

Artificial neural networks are computational models inspired by the human brain that are used in machine learning to recognize patterns and solve complex problems. The video discusses how stacking artificial neurons can lead to sophisticated tasks like face detection and classification. The speaker uses the example of an artificial neural network to illustrate the concept of machine learning and how it relates to the goal of neuromorphic computing.

💡CMOS Operations

CMOS operations refer to the digital logic design process used in modern microchips. The video mentions that by 2040, the energy required for CMOS operations in a Von Neumann architecture could be as high as 10^27 joules, which is a significant portion of the world's current energy budget. This highlights the need for more energy-efficient computing paradigms like neuromorphic computing.

💡Energy Efficiency

Energy efficiency in computing refers to the ability of a system to perform tasks with minimal energy consumption. The video emphasizes the energy efficiency of neuromorphic systems, comparing it to the human brain, which operates on about 20 watts of power. This efficiency is a key selling point for neuromorphic computing, as it could lead to more sustainable and powerful computing technologies.

💡IBM's TrueNorth

IBM's TrueNorth is a neuromorphic chip that debuted with 64 million neurons and 16 billion synapses. It is mentioned in the video as a significant milestone in the development of neuromorphic computing. The speaker highlights TrueNorth as an example of how neuromorphic systems are being developed to mimic the brain's architecture and efficiency.

💡Intel's Loihi

Intel's Loihi, referred to in the video as 'LUI', is another example of a neuromorphic chip designed to mimic the human brain's architecture. The speaker discusses how Loihi uses 64 chips to create a system of 8 million synapses, which is more efficient and faster than IBM's TrueNorth. This showcases the progress and competition in the field of neuromorphic computing.

💡Synapse

In the context of neuromorphic computing, a synapse refers to the connection points between artificial neurons, analogous to the biological synapses in the human brain. The video explains the importance of creating artificial synapses to replicate the brain's communication and data processing mechanisms. The speaker uses the concept of synapses to illustrate how neuromorphic systems aim to emulate the brain's efficiency.

Highlights

Introduction to neuromorphic computing by Orange Banerjee from Bennett University.

Neuromorphic computing aims to create devices that work like the human brain.

Historical context of computing architectures, including the von Neumann architecture.

Invention of neuromorphic engineering by Carver Mead in the 1980s.

Explanation of how neuromorphic computing mimics neurobiological architectures.

Moose Law and its implications for the growth of transistors and energy efficiency.

The limitations of von Neumann architecture and the need for neuromorphic systems.

Comparison of energy efficiency between the human brain and modern computers.

The concept of artificial neurons and how they mimic the structure of biological neurons.

Role of memristors in creating artificial synapses for neuromorphic computing.

Potential applications of neuromorphic computing in space operations and supercomputing.

Development of neuromorphic chips by companies like IBM and Intel.

IBM's TrueNorth chip with 64 million neurons and 16 billion synapses.

Intel's Loihi chip and its efficiency compared to IBM's TrueNorth.

Challenges in designing and analyzing neuromorphic systems.

The question of whether we know enough about the brain to replicate its functions in neuromorphic computers.

Closing remarks and thanks for attending the seminar.

Transcripts

[Music]

welcome to the seminar presentation my

name is orange banerjee and i'm pursuing

v-tech in cse from bennett university

well bennett university is a private

institution situated at greater noira

today's topic for presentation is

neuromorphic computing before moving

forward i would like to thank my

university and miss gagandeep kaur our

faculty for this subject in the semester

for giving me this opportunity to come

forward and explain something that i

really wanted to explore into

well i'm really interested in the very

concept of artificial intelligence and

machine learning hence neuromorphic

computing itself is a very unique

and

it's a topic that is not known by many

people so today my job here is to

explain you what exactly is neuromorphic

computing

if we go back in time

and try to realize how everything came

into this picture

or how we came to know about

neuromorphic computing then we have to

go back in 1945 or the fact that each

and every computer power and advances

were based on an architecture called the

von neumann architecture developed by

john von neumann and others

it was written on an unfinished paper

and ironically the most impressive

revolution in the history of technology

was done on basis on a half century old

design on an unfinished paper

well neuromorphic computing also known

as neuromorphic engineering was invented

by carbon med in the 1980s he talked

about the use of vlsi systems well that

is nothing but creating an integrated

circuit by combining millions of

transistors on a single chip

that consists of electronic analog

systems that replicate or mimic

neurobiological architecture present in

the human nervous system

so

in summary what i'm trying to say here

is that neuromorphic computing is

basically creating

a solid state device like our laptops

are

which would exactly work like our brain

and in order to do that we need to

understand as we move forward how our

brain works and how exactly can we do

that

well coming to the moon's law moose law

as mentioned by god mode is nothing but

the number of transistors on a microchip

would double every two years and that

the cost of computer will be halved it

has nothing to do with neuromorphic

computing but i'll tell you why i'm

getting here now the fact that

all everything is on uh the von neumann

architecture well now i've been

mentioning it too many times and i'll

tell you what exactly it is

in case of a von neumann architecture we

have a memory and a cpu and the

bottleneck is that we need to transfer

um

data

through these things

through these two

portions or sectors as you can see the

cpu and the memory however that's not

how our brain works our brain does not

transfer data from we don't have a cpu

here and a memory here that transfers

data right it's much more complex than

that so yeah so what von neumann does is

that it restricts

the development of these machines um

into something that would have higher

implications

um

coming into neural networking or

artificial neural networks or the very

concept of artificial intelligence right

so the fact is can we change the

substrate uh in the case of von neumann

architecture and make something brain

like another thing is that why i'm

mentioning moose law is that the one

human architecture is very energy hungry

as mentioned by moose law is that the

number of transistors in a microchip

would drop to double every two years it

basically speaks about the exponential

growth of

uh

of our technology in our recent

generations

by 2040 however it has been said that we

would require 10 to the power 27 joules

of energy to work on a von neumann

architecture to do cmos operations now

what is cmos operations it is nothing

but the operations that we are doing at

this current moment on every laptop

computers whatever that you take

whatever that comes into your mind right

so that is the problem here and that's

why i'm trying to tell you that the

moon's law is true however

that's what poses a huge threat

in the coming years

as

10 to the power 27 joules is currently

the budget of the entire world's energy

so you can understand the extent till

which what we are talking about here

that brings me to the point as to why do

we need moneyomorphic systems the entire

explanation

of the energy that requires the von

neumann uh the cmos operation

requires is the main reason as to why we

need neuromorphic systems right so the

thing is that uh if we if you try to

understand that there will be always be

a bottleneck or an inherent latency

right so there is this inherent latency

in the bond movement architecture for

the transfer of data to the cpu and the

memory right so the question here is the

issue here is why can't we

co-locate or why can't we have the

processor as well as uh the memory uh

together in one place

right so that that's what our brain does

right it has the processor and the

memory and it's at a single place

and it does the work unified right

so

again our brain is also very energy

efficient you need to understand that

just a banana or like a fruit or a

coffee that you drink in your daily

lives would fire you up right would

charge you up and you would be ready to

do something uh something really

tiresome right

and

this thing

works on 20 watts just that so if i'm

having a cup of coffee for example

and then i'll be fired up and ready to

do things like

uh face detection identification

and

many such things that take number of

cpus to train a machine learning model

right

and

that is

that's where the fact that we need

neuromorphic systems because on many

levels we can beat a computer our brain

can do that

so

how can we make a computer that works

like a human brain right and moving any

further forward i like to mention that

i can only scratch the surface of this

very field i can only give you the upper

layer of understanding of what or how

our neuromorphic computing works exactly

because it's a very very vast and wide

field to be honest let me give you an

example there was a research paper or a

survey that was done it was around 15 to

20 pages and the number of references on

it were 2

682

that's right that's how big of a or wide

of a field it is

so moving forward how does uh how to how

do we make a computer that works like a

human brain in order to understand that

we have to understand how a human brain

works so how

this exactly works right so let me give

you an example right here so this is

nothing but to make you understand

visually the structure of a typical

neuron and

uh artificial neuron and i'll try to

explain what it is but before moving on

to that

i'll tell you

our brain consists of neurons and

synapses we all know that

and the gaps between these neurons are

called synapses

and what happens is that data has been

transferred or these neurons communicate

with each other through

what we call neurotransmitters

and how do these work right we need to

know we these work on something called

iron flow

now ions what are ions these are nothing

but charged atoms so we've got the

potassium ion the chloride ion and

different sorts of ions present in our

brain so there are ions inside and ions

outside

hence

the flow of these ions create an

electric charge in our brain now if you

think very carefully that is exactly

what we're doing in a normal or a

typical computer right we are

controlling the flow of charge

that's how our computer works to be

honest

so

so that's that is exactly where the

analogy of neuromorphic computing comes

from

what we are trying to do is that we are

trying to replicate this exact

architecture and put it on a solid state

device or in a solid state right

and

so

what we need to understand or we need to

realize is that for that we need to

create

the synapse and artificial synapse

in order to create an artificial synapse

we need something we need an electrical

device that has a memory

in case of a strap

standard resistor you see

if you put a voltage and pass a current

through it right it doesn't have a

memory of it happening it just it takes

the current passes it through the entire

circuit or the integrated circuits in

case of computing language

so

we need something that will have the

memory of what's happening to it in the

past

so

for that there is something called

memristors and

everything that will

further talk about

every device or every

uh every invention in neuromorphic

computing that was done by hp or ibm it

was all based on these members of

devices

so my memristor is

something that has a memory of it

uh of the current passing through it

an extreme example of a memristor would

be a fuse okay so you pass the current

through it and it diffuses

but

that's not very useful right because

it's dead but you could have a very less

extreme version of it

where you pass the current then you stop

the voltage and

you know it has a memory in that state

the member still in that state has

memory of the amount of current that

you're passing through the amount of

voltage that you have put it through

so

that is the analogy of neuromorphic

computing and that is how we can build

counter neuromorphic models of the human

brain so this was a very electronic

biased discussion regarding how we can

actually make quantum neuromorphic

models or you know models that work like

a human brain let me get you into the

computational discussion of it all so as

you can

see the connectivity part of the photo

right here is that this is a

convolutional neural network

so instead of going into something as

deep as the convolutional neural network

let me start with the very basic right

so an artificial neuron in itself isn't

very effective

but if you stack them up it can do a lot

of things

like face detection

classification

and many such fields that are covered in

machine learning topics

so

in case of an artificial neutral network

what happens is that

if we talk about

uh based on rosenblatt's perceptron

right this example itself let's take

an artificial neural network that can

classify between a circle a square and a

triangle

so we have three layers the input layer

the hidden layer and the output layer

what happens is that this photo that is

28 across 28 pixels in form of matrix

grows or you know just passes through

the input layers

and they are connected through channels

to the hidden layer

and these channels are given a numerical

value called weights

and when passed through these channels

through the hidden layer the hidden

layer performs most of the computed uh

computational work that our network

requires right

so

it is passed through something called

the bias

and then the hidden layer goes through a

threshold function and this threshold

function is known as the activation

function and whatever the result of the

activation function is the one with the

highest probability is being then

reflected

however we need to understand that if we

test an artificial network directly then

it won't work we need to train it

just like we train any machine learning

model right and how do we train it we

train it by giving it or passing it

through the actual output so we feed in

the actual output

with the model itself so it compares how

many it got wrong and right and thus by

calculating the error itself it self

trains itself

providing us

a certain amount of accuracy based on

how and what we are doing right

so this is

based on this concept we are actually

trying to create an artificial synapse

so we have been talking about uh

neuromorphic systems and computers for a

long time and you know how we can create

them what is it exactly how our brain

works etc etc

but

why do we need these neuropathic systems

to be honest why or what use do these

neuropathy systems would be right

so

the answer to that question is that

neuromorphic systems have wide

implications in the near future

and let me tell you why it's because

our brain and this

is very

flexible and

it's

it's adaptive to changes right

now there is no machine learning model

or any sort of artificial intelligence

present at this very moment that can

adapt to changes or it's flexible in its

decision making and stuff like that

right

so

the promise that neuromorphic computing

holds for artificial intelligence is

massive

the major impact that

neuromorphic computings can have

is

making supercomputers faster and on

space operations now the inquiry would

be

what effect it would have on space

operations right now space missions

require high performance computing

systems

that

can you know restrict themselves to size

and weight and power

and can work in very extreme

environmental and operational conditions

right that include extreme temperature

high radiation power loss

and

what your neuromorphic computing systems

can do is that it can help

space vehicles to adapt and learn

according to its environment

and its changes hence

it would you would not require a ground

mission team to operate any space

project now this is a very huge stretch

and stride

at the same time but it is very much

possible

as you can see at this very current

moment

there is massive amounts of data that

needs to be

interpreted

by the grounds operation team in order

to

make a space mission successful right

so instead of that even though all of

that was is done on bond newman

architecture instead of that we can use

the neuromorphic computing nc's that

would actually help to reduce the number

of bytes to process images and as i've

already said you know it can

help the space vehicle in a lot of ways

and it can also

help

you know reduce manpower in general

and make the space missions are projects

much more efficient that is the major

impact of neuromorphic computing

moving on

all right so the neuromorphic

architecture right it's connecting

neuromorphic chips remember that i said

that we need members

your registers that have a memory of

working

this basically connecting neuromorphic

chips this is basically an architecture

of the membrane itself so we need to

pile these membranes in lots and lots of

piles right we need members uh

in which which are very

easily fabricable

and you can synthesize them very easily

right so you connect these members on

top of each other

and put millions of it on on a chip and

that single chip can then work as an

integrated circuit for a solid state

device or anything that we create in the

near future

so art geomorphic systems available to

us right

or have we still been able to build

neuromorphic systems right yes we have i

mean not exactly but yes we have been

able to develop neuromorphic chips

that can compute much faster and

efficiently and much closer to how our

brain works right

uh back in 2008 when members or the

missing members desires as a research

paper was titled came into effect

um many thought that that was the thing

that would have could have been used in

neuromorphic systems and that would

bring a revolution of a change

now neuromorphic systems

uh at this mo are being invested by more

and more companies nowadays because

they've come to realize that

these systems can be the future of their

entire generation

in 2014 ibm introduced something called

the true north that debuted with 64

million neurons

and ate 16 billion synapses

it was it came right out of something

called ibm's development facility

and

it was said to be one of the benchmarked

neuromorphic ships to have been ever

made till this date

however in december 2020 while we were

all in lockdown

intel introduced its neural chip known

as lui which used 64 of them of these

chips to create a system of 8 million

synapses which is much smaller than ibm

and much more faster and efficient it is

expected to reach around 100 million

euros in the near future you can

understand the amount of computing power

we are talking about here

so let me give you an example

uh ibm's uh supercomputer the summit

it can process around

let me tell you

500 pair of lobs of data and our brain

on 20 watts can process around

10 to the power 18 flops of data which

is floating points per second

operations

and

that makes our brain five times faster

than the world's largest supercomputer

so you can understand that how

important or how efficient it could be

if you can slowly slowly make

systems that are

much more

closer to how our brain works

coming on to two different things we

will talk about right now first is the

neural grid and second is ibm's true now

well as it's written here already it's a

16-chip system that emulates a millions

of neurons with billions of connection

it looks just like as you can see in the

image on the right hand side

it specifies that it mobs analog

property of the neurons of the brain by

using sub threshold analog logic

so it basically uses the very

basic or the very primitive concept of

transistors that is having the electric

analog circuits

that can actually mimic neurobiological

systems that was introduced by

scarborough men neurobreed was one of

the most oldest neuromorphic systems or

to be honest the neuromorphic

transistors to be introduced in the

market

it uses asynchronous digital logic for

communication

what it means is that it is it does not

continuously process data

it slowly divides it into small chunks

and processes data

part by part into different neurons

that's how our brain works right it

processes data and we use only a small

part of our brain not the entire part of

it to process a certain amount of data

coming on to ibm's true north it comes

from ibm's cognitive computing division

as i said it's also known as the

development division it's 16 times the

size of neurograde in 2014 ibm

introduced this even if it's

it's much bigger than what the primitive

neural grid was it is much more

efficient than the newer grid itself

and instead of the sub-threshold analog

they're completely digital that means

that it uses what i have already

mentioned a thousand times till now the

memristor devices instead of the normal

vlsi systems that were used by the

government

coming on to the challenges to using

neuromorphic systems

the basic challenge of rheomorphic

system is the design and the analysis or

the structure of it all

why i'm talking like this it's because

we

as uh like we as coders or we as people

study in computer science we know that

how tough it is to learn a new language

right made be python c plus plus or java

and these languages are what are used to

code any sort of

machine learning or you can say create a

website right so in case of a very new

technology which is the neuromorphic

system right we might have to create a

new completely new programming language

and that has a lot a lot of challenges

to it right it has new generations of

memory storage sensor technologies to be

introduced even polymorphic neuromorphic

neuromorphic systems may even require a

major change in the hardware itself as i

said we need to create a processor and

the memory at the same location

that is very tough to do ladies and

gentlemen

now

do we know enough the fundamental

question is that do we know enough can

we predict the future can we

scale

whether

uh neuromorphic computers would be as

useful as we are you know from making

them sound in today's date right for

example as it's already mentioned here

the glial cells which are the brains of

support cells right

which are not which are very prominent

in you know

in how our brain works

in how our brain processes data how

flexible our brain is however that is

not the case in any neuromorphological

designs

we do not take into consideration

many such small parts that are present

in our brain that help us take certain

decisions at certain time

because we can only think at a very

basic level such as the neurons the

synapses so on this basic level we can

create something but if we delve deep

inside

the brain itself for example the human

emotions can you replicate them that has

been one of the most biggest questions

in today's world so the question still

stands or the inquiry is still there do

we know enough about

how our brain works or we can we

replicate

enough properly

or we can make neuromorphic computers in

such a way that

it exactly mimics and replicates

what our brain does

thank you for attending the seminar

and see you guys later