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Programming Nanocells: Optimization Algorithms

Programming Nanocells: Optimization Algorithms. John Reif CS Dept, Duke University. Optimization of Nanocells. Given imperfect information about interior connectivity of nanocell (probabilistic model) Given desired I/O behavior Find pattern of control variables X input variables

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Programming Nanocells: Optimization Algorithms

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  1. Programming Nanocells: Optimization Algorithms John Reif CS Dept, Duke University

  2. Optimization of Nanocells • Given imperfect information about interior connectivity of nanocell (probabilistic model) • Given desired I/O behavior • Find pattern of control variables • X input variables • Y output variables • C control variables • Unconstrained optimization of complex linear system: Simplex C C C C C X1 C X2 Y1 C Y2 C C C C C C C C C

  3. Amoeba Algorithm • K control variables • Each control variable set to a value over range D • Search space is size DK • Modified Simplex (Amoeba) algorithms • Nelder-Mead algorithm, 1965 • Solves non-convex optimization problems • Hill-climbs K-dimensional space using K+1 simplex • Expands in opposite of least favorable direction • depends on well-designed fitness function • performs very well in many practical applications

  4. Next Steps • Use probabilistic model for nanocell • Integrate with Spice simulation of nanocell • Integrate with Matlab implementation of optimization algorithms • Experiment with implementation • program nanocell to do basic logical operations

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