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12/12 (四) 13:20-講題:「Digital Memcomputing: from logic to dynamics to topology」

Date2019-11-21

時間:2019.12.12 (四) 13:20~15:00

地點:基礎科學大樓次軒廳
講題:Digital Memcomputing: from logic to dynamics to topology

Speaker Bio: 

Massimiliano Di Ventra obtained his undergraduate degree in Physics summa cum laude from the University of Trieste (Italy) in 1991 and did his PhD studies at the Ecole Polytechnique Federale de Lausanne (Switzerland) in 1993-1997. He is now professor of Physics at the University of Californian, San Diego.

Di Ventra's research interests are in the theory of quantum transport in nanoscale and atomic systems, non-equilibrium statistical mechanics, DNA sequencing/polymer dynamics in nanopores, and memory effects in nanostructures for applications in unconventional computing and biophysics. He is the author of more than 200 scientific publications (he was named 2018 Highly Cited Researcher by Clarivate Analytics), three textbooks, and holds four U.S. patents. He has delivered more than 300 invited talks worldwide on his research, including 14 plenary/keynote presentations and 10 talks at the March Meeting of the American Physical Society.

Abstract: 

Memcomputing [1, 2] is a novel physics-based approach to computation that employs memory to both process and store information on the same physical location. Its digital version [3, 4] is designed to solve combinatorial optimization problems. A practical realization of digital memcomputing machines (DMMs) can be accomplished via circuits of non-linear, point-dissipative dynamical systems engineered so that periodic orbits and chaos can be avoided. A given logic problem is first mapped into this type of dynamical system whose point attractors represent the solutions of the original problem. A DMM then finds the solution via a succession of elementary instantons whose role is to eliminate solitonic configurations of logical inconsistency (‘‘logical defects’’) from the circuit [5, 6]. I will discuss the Physics behind memcomputing and show many examples of its applicability to various combinatorial optimization problems demonstrating its advantages over traditional approaches [7, 8]. Work supported in part by DARPA, MemComputing, Inc. (http://memcpu.com/), and CMRR.

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