Perspective: Stochastic magnetic devices for cognitive computing

Kaushik Roy, Abhronil Sengupta, Yong Shim

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Stochastic switching of nanomagnets can potentially enable probabilistic cognitive hardware consisting of noisy neural and synaptic components. Furthermore, computational paradigms inspired from the Ising computing model require stochasticity for achieving near-optimality in solutions to various types of combinatorial optimization problems such as the Graph Coloring Problem or the Travelling Salesman Problem. Achieving optimal solutions in such problems are computationally exhaustive and requires natural annealing to arrive at the near-optimal solutions. Stochastic switching of devices also finds use in applications involving Deep Belief Networks and Bayesian Inference. In this article, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the computational units of such probabilistic intelligent systems.

Original languageEnglish (US)
Article number210901
JournalJournal of Applied Physics
Volume123
Issue number21
DOIs
StatePublished - Jun 7 2018

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this