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Ovonic Cognitive Computer, LLC formed 9/26/2002

Ovonic Cognitive Computer, LLC formed 9/26/2002. Stan Ovshinsky Helmutt Fritsche Morel Cohen Dave Strand. Guy Wicker Boil Pashmakov Pat Klersy Robert Miller. The problem with computers. Memory. Processor.

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Ovonic Cognitive Computer, LLC formed 9/26/2002

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  1. Ovonic Cognitive Computer, LLC formed 9/26/2002 Stan Ovshinsky Helmutt Fritsche Morel Cohen Dave Strand Guy Wicker Boil Pashmakov Pat Klersy Robert Miller

  2. The problem with computers Memory Processor Data Transmission Processor is too slow Memory access is too slow Not enough memory

  3. Memory Heirarchy gives unlimited Memory, but leads to “bottlenecks” Processor Cache DRAM SSD NAND HDD cloud slow 10 nanoseconds 60 nanoseconds 1 millisecond 1 second 10-100 second 128 Megabytes 8 Gigabytes 512 Gigabytes 10 terrabytes infinity Obfuscation of origin 3DXpoint bridges the gap between DRAM and SSD

  4. 3DXpoint is Ovonic Threshold and Memory switches connected ECD 1970 3D version ECD 1987

  5. 3DXpoint solves the “bottleneck”, speeding up access 10 to 100X But processors are still too slow Memory Processor Data Transmission GPU Data Transmission Graphics processors are now widely employed to speed large, data intensive operations

  6. Graphics Processors are 100X faster than normal microprocessors But they require more data transmission and more memory Nvidia Jetson TX2 GPU $600 Connect 6 video cameras to it and it can drive a car unaided. It can do 1.2 trillion calculations per second

  7. GPUs drive cars, recognize any human language, and analyze huge amounts of data using ~1000X1000X8 Neural Network emulation They emulate the function of a neuron in a network of neurons. It takes an enormous amount of calculation to model each neuron and sequentially calculate the entire network. The Ovonic Cognitive Computer elements have similar function to a neuron. They don’t emulate neural networks, they actually are Neural networks.

  8. Neurosynaptic Cell Ovonic Single and Multiple Cells have the same properties as neurons and biological cells Dendrites Output fires when the threshold is reached by summing the inputs Nucleus Axon Inputs saturate with a sigmoidal response Neural Network computing Uses a nerve-like connected array of elements with saturating, weighted inputs and outputs when a threshold is reached

  9. Ovonic Threshold Device Ovonic Memory Device Ovonic switches can implement a Physical Neural Network • Switching in chalcogenide materials based on lone-pair excitation: • Threshold --- noncrystallizing --- OTS • Memory --- phase change --- OMS The voltage and current characteristics can be tailored for the requirements of the application This is the only practical device that works. RRAMs, Memristors and quantum computing devices are being researched. Ovonic devices are now commercial products.

  10. DEVICE RESISTANCE (Ohms) PROGRAMMING VOLTAGE (V) Ovonic Memory Multi-State Data Storage For Storage: Multiple bits per cell increases storage density and reduces cost For Processing: The analog characteristic provides ideal means for synaptic weighting

  11. Ovonic Multi-Terminal Threshold Device This simplifies control in a synaptic environment

  12. The Ovonic Cognitive Computer

  13. Ovonic Cognitive Device Operation

  14. Well over 1000 startups are pursuing cognition with >$20B invested

  15. September 10 - 16, 2017 Aachen, Germany 2016 2011

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