Memcomputing: a brain-inspired topological computing paradigm
11:15 - 12:15
Massimiliano Di Ventra
University of California, San Diego, USA
CFEL (Bldg. 99)
Seminar Room V, O1.109
Which 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 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 . We have named this novel computing paradigm “memcomputing” . As an example, I will show the polynomial-time solution of prime factorization and the NPhard version of the subset-sum problem using polynomial resources and selforganizing logic gates, namely gates that self-organize to satisfy their logical proposition . I will also show that these machines are described by a Witten-type topological field theory and they compute via an instantonic phase where a transient long-range order develops due to the effective breakdown of topological supersymmetry . The digital memcomputing machines that we propose are scalable and can be easily realized with available nanotechnology components, and may help reveal aspects of computation of the brain.
 F. L. Traversa and M. Di Ventra, Universal Memcomputing Machines, IEEE Transactions on Neural Networks and Learning Systems 26, 2702 (2015).
 M. Di Ventra and Y.V. Pershin, Computing: the Parallel Approach, Nature Physics 9, 200 (2013).
 F. L. Traversa and M. Di Ventra, Polynomial-time solution of prime factorization and NP-hard problems with digital memcomputing machines, arXiv:1512.05064.
 M. Di Ventra, F. L. Traversa and I.V. Ovchinnikov, Topological field theory and computing with instantons (in preparation).