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Computing, Neurons and National Security

Explore the intersection of computing, neurons, and national security, and the potential of converging technologies in the Cogno, Nano, Bio, and Info domains. Discover how this convergence can address national security concerns and restore exponential rates of improvement.

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Computing, Neurons and National Security

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  1. Computing, Neurons and National Security Rob Leland Director, Computing Research Sandia National Labs. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND2013-1541C

  2. Moore’s Law: “In fact, shrinking dimension on an integrated structure makes it possible to operate the structure at a higher speed for the same power per unit area” The motivating circumstance Density Log scales (see labels) Power Efficiency Clock Rate2 ‘60 ‘70 ‘80 ‘90 ‘00 ‘10 ‘20 Year Cramming more components onto integrated circuits, Gordon E. Moore, April, 1965 8/14/2012 24

  3. Reflections on this circumstance • Moore’s Law represents the commoditization of the Turing/von Neumann vision • National security was the early driver of big computing, and also depended intimately on it • Hydrogen bomb simulations • Secure and defeat communications • Ballistics tables calculations • Weather and climate simulations • Moore’s Law will likely peter out within the decade • We are seeking a new approach that • Embraces national security concerns • Restores exponential rates of improvement (implying commoditization) • May not follow the Turing/von Neumann model

  4. The national security landscape Global Environmental War • Conventional engage. • Nuclear deterrence • Military containment • Resource conflicts • Sustainable dev. • Ecomigration Constructive Destructive Human need Homeland • Terrorism • Crime • Prosperity • Health care • Education Local

  5. Technology “convergence” Decreasing cycle time & increasing synergy

  6. Cogno Cogno Nano Nano Bio Bio Info Info One conjecture

  7. Cogno Nano Bio Info Edge examples Neural interface Neural hardware Neural networks Cellular instrumentation Cellular sensors Cellular signaling

  8. Cogno Nano Bio Info Plane examples Neural “nMOS” architecture (Tokyo U) Neural interface Live neural net (Cornell) Neural hardware Neural networks Cellular instrumentation Cellular sensors Cellular signaling model • Neuron “tunnel” (SNL) • “Digital” antibodies … viral sized logic chips (HP) • Tissue based and in-vivo sensors • - nanotube monitors in brain blood vessels (NYU) • - optical nanosensors for chemical analysis within cells (U Mich.) • DNA regulated nano assembly of circuit devices (U Minn.)

  9. Cogno Nano Bio Info Neuro-Inspired computing: a (very) notional roadmap Behavioral Cognitive prosthetics Synthetic intelligence Neural “nMOS” architecture (Tokyo U) Neural interface Live neural net (Sandia) Neural hardware Neural networks Cellular instrumentation Brain-machine interface Artificial immune system Cellular sensors Cellular signaling model • “Digital” antibodies … viral sized logic chips (HP) • Tissue based and in-vivo sensors • - nanotube monitors in brain blood vessels (NYU) • - optical nanosensors for chemical analysis within cells (U Mich.) • DNA regulated nano assembly of circuit devices (U Minn.) Physical

  10. Acknowledgements • Moore’s Law: Derived from Erik Debenedictis, Computing Research, Sandia, 2012 • National Security Landscape: Derived from Gerry Yonas, Sandia’s Advanced Concepts Group, Sandia, ca. 2000 • Technology convergence: NSF report on first NBIC Convergence conference, 6/02 • The Cognohedron: Indialogue with colleagues in Sandia’s Advanced Concepts Group, ca. 2000

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