5 research outputs found
The Department of Electrical and Computer Engineering Newsletter
Summer 2017
News and notes for University of Dayton\u27s Department of Electrical and Computer Engineering.https://ecommons.udayton.edu/ece_newsletter/1010/thumbnail.jp
Spintronics-based Reconfigurable Ising Model Architecture
Published in the International Symposium On Quality Electronic Design (ISQED), March 2020The Ising model has been explored as a framework for modeling NP-hard problems, with several diverse systems proposed to solve it. The Magnetic Tunnel Junction (MTJ)-based Magnetic RAM is capable of replacing CMOS in memory chips. In this paper, we propose the use of MTJs for representing the units of an Ising model and leveraging its intrinsic physics for finding the ground state of the system through annealing. We design the structure of a basic MTJ-based Ising cell capable of performing the functions essential to an Ising solver. A technique to use the basic Ising cell for scaling to large problems is described. We then go on to propose Ising-FPGA, a parallel and reconfigurable architecture that can be used to map a large class of NP-hard problems, and show how a standard Place and Route tool can be utilized to program the Ising-FPGA. The effects of this hardware platform on our proposed design are characterized and methods to overcome these effects are prescribed. We discuss how two representative NP-hard problems can be mapped to the Ising model. Simulation results show the effectiveness of MTJs as Ising units by producing solutions close/comparable to the optimum, and demonstrate that our design methodology holds the capability to account for the effects of the hardware.This work was supported by the National Science Foundation(NSF) under Grant 164242
Posiform Planting: Generating QUBO Instances for Benchmarking
We are interested in benchmarking both quantum annealing and classical
algorithms for minimizing Quadratic Unconstrained Binary Optimization (QUBO)
problems. Such problems are NP-hard in general, implying that the exact minima
of randomly generated instances are hard to find and thus typically unknown.
While brute forcing smaller instances is possible, such instances are typically
not interesting due to being too easy for both quantum and classical
algorithms. In this contribution, we propose a novel method, called posiform
planting, for generating random QUBO instances of arbitrary size with known
optimal solutions, and use those instances to benchmark the sampling quality of
four D-Wave quantum annealers utilizing different interconnection structures
(Chimera, Pegasus, and Zephyr hardware graphs) as well as the simulated
annealing algorithm. Posiform planting differs from many existing methods in
two key ways. It ensures the uniqueness of the planted optimal solution, thus
avoiding groundstate degeneracy, and it enables the generation of QUBOs that
are tailored to a given hardware connectivity structure, provided that the
connectivity is not too sparse. Posiform planted QUBOs are a type of 2-SAT
boolean satisfiability combinatorial optimization problems. Our experiments
demonstrate the capability of the D-Wave quantum annealers to sample the
optimal planted solution of combinatorial optimization problems with up to
qubits