TY - JOUR
T1 - Biased Random Walk Using Stochastic Switching of Nanomagnets
T2 - Application to SAT Solver
AU - Shim, Yong
AU - Sengupta, Abhronil
AU - Roy, Kaushik
N1 - Funding Information:
Manuscript received December 8, 2017; revised January 29, 2018; accepted February 14, 2018. Date of publication March 6, 2018; date of current version March 22, 2018. This work was supported in part by the Center for Spintronic Materials, Interfaces, and Novel Architectures, in part by MARCO and DARPA sponsored StarNet Center, in part by Semiconductor Research Corporation, in part by the National Science Foundation, in part by Intel Corporation, and in part by the National Security Science and Engineering Faculty Fellowship. The review of this paper was arranged by Editor F. Schwierz. (Corresponding author: Yong Shim.) The authors are with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA (e-mail: shim13@purdue.edu).
Publisher Copyright:
© 2012 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - Random walk (RW)-based local search algorithms are highly popular for solving combinatorial optimization problems such as the satisfiability (SAT) problem. The RW algorithm tries to solve the SAT problem by flipping a randomly chosen variable to minimize the number of unsatisfied clauses for a given problem. In this paper, we propose a biased RW (BRW) based on stochastic magnetization switching dynamics of nanomagnets in the presence of thermal noise. The controllable stochastic switching behavior of nanomagnets is used to flip the current state of the variable of interest based on the assigned probability. Here, the flipping probabilities are assigned to the variables responsible for unsatisfied clauses (instead of deterministic flipping in traditional algorithms). The stochasticity of individual units of the proposed hardware SAT solver based on a magnetic tunnel junction lying on top of a heavy metal layer enables parallel search of the solution space, which results in rapid convergence in comparison to the baseline RW algorithm. A device-circuit-algorithm cosimulation framework (benchmarked to experimental measurements of a magnetic stack) is used to assess the efficiency of the proposal. In comparison to the baseline RW algorithm, stochastic magnetization switching-driven BRW achieves 94% reduction in search time while consuming 30 pJ energy per iteration.
AB - Random walk (RW)-based local search algorithms are highly popular for solving combinatorial optimization problems such as the satisfiability (SAT) problem. The RW algorithm tries to solve the SAT problem by flipping a randomly chosen variable to minimize the number of unsatisfied clauses for a given problem. In this paper, we propose a biased RW (BRW) based on stochastic magnetization switching dynamics of nanomagnets in the presence of thermal noise. The controllable stochastic switching behavior of nanomagnets is used to flip the current state of the variable of interest based on the assigned probability. Here, the flipping probabilities are assigned to the variables responsible for unsatisfied clauses (instead of deterministic flipping in traditional algorithms). The stochasticity of individual units of the proposed hardware SAT solver based on a magnetic tunnel junction lying on top of a heavy metal layer enables parallel search of the solution space, which results in rapid convergence in comparison to the baseline RW algorithm. A device-circuit-algorithm cosimulation framework (benchmarked to experimental measurements of a magnetic stack) is used to assess the efficiency of the proposal. In comparison to the baseline RW algorithm, stochastic magnetization switching-driven BRW achieves 94% reduction in search time while consuming 30 pJ energy per iteration.
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U2 - 10.1109/TED.2018.2808232
DO - 10.1109/TED.2018.2808232
M3 - Article
AN - SCOPUS:85043376908
VL - 65
SP - 1617
EP - 1624
JO - IEEE Transactions on Electron Devices
JF - IEEE Transactions on Electron Devices
SN - 0018-9383
IS - 4
ER -