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SUMMARY:Memcomputing: a brain-inspired computing paradigm *Note: actual end time may vary.*
UID:124340-ucb-events-calendar@berkeley.edu
ORGANIZER;CN="UC Berkeley Calendar Network":
LOCATION:560 Evans Hall
DESCRIPTION:Massimiliano Di Ventra\, Dept of Physics\, UC San Diego\n\nWhich features make the brain such a powerful and energy-efficient computing machine? Can we reproduce them in the solid state\, and if so\, what type of computing paradigm would we obtain? I will show that a machine that uses memory (time non-locality) to both process and store information\, like our brain\, and is endowed with intrinsic parallelism and information overhead – namely takes advantage\, via its collective state\, of the network topology related to the problem – has a computational power far beyond our standard digital computers [1\, 2]. We have named this novel computing paradigm “memcomputing” [1\, 2\, 3\, 4]. As examples\, I will show the polynomial-time solution of prime factorization\, the search version of the subset-sum problem [5]\, and approximations to the Max-SAT beyond the inapproximability gap [6] using polynomial resources and self-organizing logic gates\, namely gates that self-organize to satisfy their logical proposition [5]. I will also show that these machines are described by a topological field theory\, and they compute via an instantonic phase\, implying that they are robust against noise and disorder [7]. The digital memcomputing machines we propose can be efficiently simulated\, are scalableand can be easily realized with available nanotechnology components. Work supported in part by CMRR and MemComputing\, Inc. (http://memcpu.com/).\n\n[1] M. Di Ventra and Y.V. Pershin\, Computing: the Parallel Approach\, Nature Physics 9\, 200 (2013).\n[2]F. L. Traversa and M. Di Ventra\, Universal Memcomputing Machines\, IEEE Transactions on Neural Networks and Learning Systems 26\, 2702 (2015).\n[3] M. Di Ventra and Y.V. Pershin\, Just add memory\, Scientific American 312\, 56 (2015).\n[4] M. Di Ventra and F.L. Traversa\, Memcomputing: leveraging memory and physics to compute efficiently\, J. Appl. Phys. 123\, 180901 (2018).\n[5] F. L. Traversa and M. Di Ventra\, Polynomial-time solution of prime factorization and NP-complete problems with digital memcomputing machines\, Chaos: An Interdisciplinary Journal of Nonlinear Science 27\, 023107 (2017). \n[6] F. L. Traversa\, P. Cicotti\, F. Sheldon\, and M. Di Ventra\, Evidence of an exponential speed-up in the solution of hard optimization problems\,Complexity 2018\, 7982851 (2018).\n[7] M. Di Ventra\, F. L. Traversa and I.V. Ovchinnikov\, Topological field theory and computing with instantons\, Annalen der Physik 1700123 (2017).
URL:http://events.berkeley.edu/index.php/calendar/sn/pubaff.html?event_ID=124340&view=preview
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