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Muhammad Nauman EE 01083 060 Bio Medical Instrumentation THINKING LIKE A HUMAN – WITH MEMRISTORS.

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Presentation on theme: "Muhammad Nauman EE 01083 060 Bio Medical Instrumentation THINKING LIKE A HUMAN – WITH MEMRISTORS."— Presentation transcript:

1 Muhammad Nauman EE Bio Medical Instrumentation THINKING LIKE A HUMAN – WITH MEMRISTORS

2 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.

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4 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 trillion 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 artificial 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 synaptic inputs per neuron. THE GREAT BRAIN RACE - HISTORY

5  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

6 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.

7  A true artificial intelligence 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?

8  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”

9 STRUCTURE OF MEMRISTOR

10  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

11 Human CortexDARPA SyNAPSE Hardware Goals Cortical-Scale Hardware System About 10 6 neurons per square centimeter About synapses per square centimeter About 2 milliwatts per square centimeter Total power consumption: 20 watts 10 6 neurons (neuron cores) per square centimeter synapses per square centimeter (memristors) About 100 milliwatts per square centimeter Total power consumption: 1 kilowatt neuromorphic chips “neurons” “synapses” Total power consumptioin: 1 kilowatt MONETA: A MIND MADE OF MEMRISTORS

12  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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