Artificial BRAIN Using Nanotechnology

8/8/2019 Artificial BRAIN Using Nanotechnology

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Artificial Brain using

Nanotechnology

By

Swapnil R.Vairale

T.Y.B.Tech-E&Tc

Roll No:-704055


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Why Artificial Brain??

Although traditional, digital computers haveconsistently increased in speed and complexity

But limited by the von Neumann bottleneck

(sequential processing of instructions and aseparation between (CPU) & memory)

They are "a word-at-a-time" devices

One of today's most sophisticated supercomputer,IBM's Clue Gene/P, performs 83 times slower than acat's brain, but it's a massive machine with morethan 147,000 CPUs, 144 terabytes of memory and adedicated power supply.


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Is it possible??

An artificial human brain is science fiction, you areprobably right for now

Neuromorphic engineering a branch totally

dedicated for development of artificial brain


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What is neuromorphic engineering ??

A new interdisciplinary discipline

Includes nanotechnologies and whose goal is todesign artificial neural systems with physical

architectures similar to biological nervous systems


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Challenges in Neuromorphic Engineering

A synapse is a structure that permits a neuron to pass an electrical orchemical signal to another cell.

A neuron is an electrically excitable cell that processes and transmitsinformation by electrical and chemical signaling via synapses with other

cells.

Human brain contains vastly more synapses than neurons by a factor ofabout 10,000

Hence the necessity to develop a nanoscale, low power, synapse-like

device


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Artificial Synapses

Can be realized by development of a Hybridnanoparticle-organic transistor that canmimic the main functionalities of a synapse

Also MEMRISTER devices are capable ofemulating the biological synapses with properlydesigned CMOS neuron components


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Memrister

A two-terminal electronic device whoseconductance can be precisely modulated by chargeor flux through it.

Has the special property that its resistance can beprogrammed (resistor) and subsequently remainsstored (memory).

Can provide interface between conventional cktsand artificial & connect conventional ckts andsupport a process that is the basis for memory andlearning in biological systems


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Synapse & Memristor

The synaptic weight modulates how signals aretransmitted between neurons and can in turn beprecisely adjusted by the ionic flow through thesynapse.

A memristor by definition is a resistive device withinherent memory. It is in fact very similar to asynapse they are both two-terminal deviceswhose conductance can be modulated by external

stimuli with the ability to store (memorize) the newinformation


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Fig(a) shows the concept ofusing memristors as synapsesbetween neurons.

The insets show theschematics of the two-terminal device geometry andthe layered structure of thememristor.

Memristor as a Synapse


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Fig(b) shows a neuromorphicwith CMOS neurons andmemristor synapses in acrossbar configuration.

In this setup, the siliconmemristor consists of a pairof electrodes sandwiching anamorphous-silicon layerdoped with silver atoms, withhigh silver concentration nearthe top electrode and low

silver concentration near thebottom electrode.



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The new conductance state is maintained until the next voltage pulse isapplied.

By controlling the silver doping profile and other device parameters,scientists were able to show that the change in the memristorconductance is proportional to the time integral of the voltage appliedacross it.

In other words, the device state is not determined by the existing signals

but by the history of the applied signals

When a positive voltage is appliedacross the memristor, silver ions inthe silicon layer will drift to thebottom electrode and increasethe overall conductance of the

device, and vice versa


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Furthermore, this property allows us to preciselycontrol the memristor conductance with externalstimuli the longer the voltage pulse is appliedacross the memristor the larger the conductancechange is. These properties essentially enable thememristor to mimic synaptic action.

An electrical circuit consisting of CMOS 'neurons'and memristor synapses can achieve spike-timing

dependent plasticity (STDP), an important synapticactivity.
http://en.wikipedia.org/wiki/Spike-timing-dependent_plasticityhttp://en.wikipedia.org/wiki/Spike-timing-dependent_plasticityhttp://en.wikipedia.org/wiki/Spike-timing-dependent_plasticityhttp://en.wikipedia.org/wiki/Spike-timing-dependent_plasticityhttp://en.wikipedia.org/wiki/Spike-timing-dependent_plasticityhttp://en.wikipedia.org/wiki/Spike-timing-dependent_plasticity


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What is exciting about these results is that it showsthat memristors can behave just like synapses

These findings show that it is now possible to build

a brain-like computer using electronic components,namely, transistors and memristors.

The key is to realize the similarity betweensynapses and memristors


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Applications of Neuromorphology

To build brain-like computers using Memristors

Besides the above to build high density storagememory

New approaches to build circuits may be developedso that the increase in computing power does notcome from the increase in raw device speed (clockfrequency) but comes from the increase incomputing efficiency instead


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References & Bibliography

http://www.nanowerk.com/

www.blogcdn.com/

http://en.wikipedia.org/wiki/Memristor

http://www.google.co.in/
http://www.nanowerk.com/http://www.blogcdn.com/http://en.wikipedia.org/wiki/Memristorhttp://www.google.co.in/http://www.google.co.in/http://en.wikipedia.org/wiki/Memristorhttp://www.blogcdn.com/http://www.blogcdn.com/http://www.nanowerk.com/


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References & Bibliography

Research Paper at the University of Michigan (UM)

"Nanoscale Memristor Device as Synapse inNeuromorphic Systems( published in

NANOLETTERS magazine)

Research Paper by Hewlett-Packard Labs"Memristive switches enable stateful logicoperations via material implication(published inNATURE magazine)
http://dx.doi.org/doi:10.1021/nl904092hhttp://dx.doi.org/doi:10.1021/nl904092hhttp://dx.doi.org/doi:10.1021/nl904092hhttp://dx.doi.org/doi:10.1038/nature08940http://dx.doi.org/doi:10.1038/nature08940http://dx.doi.org/doi:10.1038/nature08940http://dx.doi.org/doi:10.1038/nature08940http://dx.doi.org/doi:10.1038/nature08940http://dx.doi.org/doi:10.1038/nature08940http://dx.doi.org/doi:10.1021/nl904092hhttp://dx.doi.org/doi:10.1021/nl904092hhttp://dx.doi.org/doi:10.1021/nl904092hhttp://dx.doi.org/doi:10.1021/nl904092h


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Thank You!!!!!!!

Artificial BRAIN Using Nanotechnology

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