Summary Presentation of Cortical Computing with Memrisitve Nanodevices Authored by Greg S. Snider Hewlett-Packard Laboratories Published Winter 2008, SciDAC.

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Summary Presentation of Cortical Computing with Memrisitve Nanodevices

Presentation transcript:

Summary Presentation of Cortical Computing with Memrisitve Nanodevices Authored by Greg S. Snider Hewlett-Packard Laboratories Published Winter 2008, SciDAC Review Presented by: Joseph Lee Greathouse

Overview of Topics Quick refresher on real neurons Reminder of artificial neural networks Memristors Memristor-based neural circuits “Cortical Computing” 2

Neurons are nervous system cells 3 Neuron body receives signals from other neurons Synapse is connection point between neurons Synapse can be electrical or chemical “All-or-none” principle

More detailed makeup of a neuron 4

Artificial Neural Networks Computational model that simulates real neurons Inputs send signals through hidden layer Hidden layer weighs inputs and gives outputs based on those inputs This yields actions akin to neurons 5

Artificial Neural Net Example Artificial Neural Networks can be used to do full-fledged computations This perceptron example requires designer to know a truth table. Would like to have a circuit that trains itself based on examples 6

Pattern Recognition ANN 7 “Neurons” “Synapses” Pattern Excitatory inputs Inhibitory inputs Categorization

Memristors Passive two-terminal devices Basically: Variable resistance as a function of voltage previously applied 8

Memristor Learning Behavior 9

Utilizing Memristors for ANN Synapses ANNs can use memristors as artificial synapses Resistance changes signal strength between input & output neurons Memristors allow learning to take place utilizing only two terminals 10

Artificial neurons as a circuit 11 Two inputs (inhibitory/excitatory) Two outputs (inhibitory/excitatory) Some calculation core Example of this as a computational neuron with memristors

Building circuit on silicon Lay out circuit to use multiple metal layers Each neuron circuit fabbed in regular CMOS 12

Layout of an entire neural network Build entire cortex structure using Neurons (boxes/circuits), axons & dendrites (wires), and synapses (memristors/wire crossings). 13

Neural circuitry doing pattern recognition 14

Cortical Computing "People confuse these kinds of networks with neural networks," … But neural networks - the previous best hope for creating an artificial brain - are software working on standard computing hardware. "What we're aiming for is actually a change in architecture," -- from “Memristor minds: The future of artificial intelligence” 15