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Sutton B, Camsari KY, Behin-Aein B, et al. Intrinsic optimization using stochastic nanomagnets. Sci Rep. 2017;7:44370doi: 10.1038/srep44370.
Sutton, B., Camsari, K. Y., Behin-Aein, B., & Datta, S. (2017). Intrinsic optimization using stochastic nanomagnets. Scientific reports, 744370. https://doi.org/10.1038/srep44370
Sutton, Brian, et al. "Intrinsic optimization using stochastic nanomagnets." Scientific reports vol. 7 (2017): 44370. doi: https://doi.org/10.1038/srep44370
Sutton B, Camsari KY, Behin-Aein B, Datta S. Intrinsic optimization using stochastic nanomagnets. Sci Rep. 2017 Mar 15;7:44370. doi: 10.1038/srep44370. PMID: 28295053; PMCID: PMC5353626.
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Sci Rep. 2017 Mar 15;7:44370. doi: 10.1038/srep44370.

Intrinsic optimization using stochastic nanomagnets.

Scientific reports

Brian Sutton, Kerem Yunus Camsari, Behtash Behin-Aein, Supriyo Datta

Affiliations

  1. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
  2. GLOBALFOUNDRIES Inc., Santa Clara, CA 95054, USA.

PMID: 28295053 PMCID: PMC5353626 DOI: 10.1038/srep44370

Abstract

This paper draws attention to a hardware system which can be engineered so that its intrinsic physics is described by the generalized Ising model and can encode the solution to many important NP-hard problems as its ground state. The basic constituents are stochastic nanomagnets which switch randomly between the ±1 Ising states and can be monitored continuously with standard electronics. Their mutual interactions can be short or long range, and their strengths can be reconfigured as needed to solve specific problems and to anneal the system at room temperature. The natural laws of statistical mechanics guide the network of stochastic nanomagnets at GHz speeds through the collective states with an emphasis on the low energy states that represent optimal solutions. As proof-of-concept, we present simulation results for standard NP-complete examples including a 16-city traveling salesman problem using experimentally benchmarked models for spin-transfer torque driven stochastic nanomagnets.

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