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Muhammad Nauman
EE 01083 060
Bio Medical Instrumentation
THINKING LIKE A
HUMAN – WITH
MEMRISTORS
Is there any difference between human
brain and computer ?
HARDWARE VS. WETWARE
Brain:
• Storage and computation
happen at the same time and
in the same place.
• Importance of information is
evaluated by contrasting it with
previous state of synapse
between Neurons.
• Computation takes during the
information transfer.
Computer:
• Memory and processor are physically
separated – a physical distance exists.
• Steps to model a single synapse:
• Synapse’s state is located in the
main memory.
• Signal originates in processor and
packed to travel on bus for about 2
to 10 centimeters.
• Reaches memory and unpacked to
actually access the memory.
• Such sequence is multiplied with
8000 to build a single neuron of rat.
BLUE BRAIN: In 2005, Henry Markram along with his team used an IBM supercomputer to simulate one square centimeter of cerebral cortex.
C2: In 2009, IBM Almaden, the C2 simulator re-creates 1 billion neurons connected by 10 tril l ion individual synapses , or about the amount found in a small mammal.
NEUROGRID: Kwabena Boahen at Stanford is developing a silicon chip. One of the goals of this research is to build artif icial retinas to be used as medical implants for the blind.
IFAT 4G: At Johns Hopkins University, implemented a visual cortex model for object recognition.
BRAINSCALES: In the European Union's neuromorphic chip program, Started in January 2011, the non–von Neumann hardware included a complex neuron model with up to 16 000 synaptic inputs per neuron.
THE GREAT BRAIN RACE - HISTORY
On a standard computer, the memory and processor are
separated by a data channel or bus.
Data bus (channels) have fixed capacity.
Processor reserves a small number of slots called registers
for storing data during computation, the processor writes the
results back to memory. Though modern processors have
cache memory.
Simple brain have tens of millions of neurons connected by
billions of synapses, any attempt to simulate their
interconnection and computational power requires a cache
memory of processor as big as the computer’s main
memory.
High power consumption in contrast with brain which can
operate at around 100 millivolts in most crucial state.
MAJOR PROBLEMS WITH COMPUTER
TO BE USED AS MAMMALIAN BRAIN
Why a biological brain is able to quickly execute this
massive simultaneous information?
NEUROMORPHIC ARCHITECTURE
Here's what happens in a brain:
(Consider two neurons; Neuron 1 and Neuron 2)
Neuron 1 gives an impulse, and the resultant information is sent
down the axon to the synapse of its target, Neuron 2.
The synapse of Neuron 2, having stored its own state locally,
evaluates the importance of the information coming from Neuron 1
by integrating it with its own previous state and the strength of its
connection to Neuron 1.
Then, these two pieces of information—the information from
Neuron 1 and the state of Neuron 2's synapse—flow toward the
body of Neuron 2 over the dendrites.
And here is the important part:
By the time that information reaches the body of
Neuron 2, there is only a single value—all processing has
already taken place during the information transfer. There is
never any need for the brain to take information out of one
neuron, spend time processing it, and then return it to a
different set of neurons.
Instead, in the mammalian brain, storage and processing
happen at the same time and in the same place. Computer
scientists call it a neuromorphic architecture.
A true artificial intell igence could hypothetically run on conventional hardware, but it would be fantastically inefficient.
“So how do you build something that has an architecture like the brain's? “
Change the architecture to merge memory and computation .
“Memristor” is the best technology.
The concept wasn’t new. The concept wasn't new. In 1971, professor Leon Chua of the University of California, Berkeley, reasoned that a memristor would behave like a resistor with a conductance that changed as a function of its internal state and the voltage applied.
“a memristor could remember how much current had gone through it, it could work as an essentially nonvolatile memory .”
In 2008, HP labs had created a functioning memristor.
HOW TO BUILD NEUROMORPHIC
ARCHITECTURE?
A memristor is a two-terminal device whose resistance changes depending on the amount, direction, and duration of voltage that's applied to it.
But here's the really interesting thing about a memristor: Whatever its past state, or resistance, it freezes that state until another voltage is applied to change it. Maintaining that state requires no power. That's different from a dynamic RAM cell , which requires regular charge to maintain its state .
A memristor is a two-terminal device whose resistance changes depending on the amount, direction, and duration of voltage that's applied to it. But here's the really interesting thing about a memristor: Whatever its past state, or resistance, it freezes that state until another voltage is applied to change it. Maintaining that state requires no power. That's different from a dynamic RAM cell , which requires regular charge to maintain its state.
FOURTH FUNDAMENTAL ELECTRONICS
COMPONENT “MEMRISTOR”
STRUCTURE OF MEMRISTOR
Memristor's "state" can be considered analogous to the state
of the synapse as they preserve the previous amount of
current passing from them.
The state of the synapse depends how closely any two neurons
are linked, which is a key part of the mammalian ability to
learn new information.
MEMRISTOR VS. SYNAPSES
Human Cortex DARPA SyNAPSE
Hardware Goals
Cortical-Scale Hardware
System
• About 106 neurons per
square centimeter
• About 1010 synapses
per square centimeter
• About 2 milliwatts per
square centimeter
• Total power
consumption: 20 watts
• 106 neurons (neuron
cores) per square
centimeter
• 1010 synapses per
square centimeter
(memristors)
• About 100 milliwatts
per square centimeter
• Total power
consumption: 1
kilowatt
• 10 000 neuromorphic
chips
• 1010 “neurons”
• 1016 “synapses”
• Total power
consumptioin: 1
kilowatt
MONETA: A MIND MADE OF MEMRISTORS
The use of the memristor addresses the basic hardware
challenges of neuromorphic computing: the need to
simultaneously move and manipulate data, thereby drastically
cutting power consumption and space.
Neuromorphic computation means computation that can be
divided up between hardware that processes like the body of a
neuron and hardware that processes the way dendrites and
axons do.
CONCLUSION
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