STATICA: A 512-Spin 0.25M-Weight Annealing Processor With an All-Spin-Updates-at-Once Architecture for Combinatorial Optimization With Complete Spin–Spin Interactions

Yamamoto, Kasho; Kawamura, Kazushi; Ando, Keiichi; Mertig, Normann; Tokuda, Takashi; Yamaoka, Masanao; Teramoto, Hiroshi; Sakai, Akira; Takamaeda-Yamazaki, Shinya; Motomura, Masato

doi:10.1109/jssc.2020.3027702

Cited by 76 publications

(57 citation statements)

References 19 publications

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“…We implemented 2048-spin-size bSB and dSB machines (bSBM and dSBM) using single FPGAs (see section S8 for details) and solved K 2000 by them. Figure 2B shows the comparison between our machines and the above three other machines (11,27,30), where the lines and the crosses show the average values of our machines and the others, respectively, for 100 trials, and the bars show the maximum and minimum values among the 100 trials. (The bars for our machines are shown at only typical computation times.)…”

Section: Performance For a 2000-spin Ising Problem With All-to-all Connectivitymentioning

confidence: 99%

“…Such Ising machines are believed to be broadly useful, because the Ising problem belongs to the nondeterministic polynomial time (NP)-complete class (7), and consequently, many combinatorial optimization problems can be reduced to the Ising problem (8). Various Ising machines other than QA have been developed, including coherent Ising machines (CIMs) implemented with pulse lasers (9)(10)(11)(12)(13)(14) and other kinds of optical Ising machine (15)(16)(17)(18), as well as digital processors based on simulated annealing (SA) (19)(20)(21)(22)(23), which is a standard heuristic algorithm for combinatorial optimization (1,24,25), or other recently proposed algorithms (26)(27)(28)(29).…”

Section: Introductionmentioning

confidence: 99%

“…This result suggests that parallelizability is a key property of optimization algorithms for their acceleration by fully exploiting modern high-performance computing systems. In this direction of research, other parallelizable algorithms have also recently been proposed by mapping a given problem to a bipartite one and applying parallel SA updating to each group of spins (26)(27)(28). These new algorithms essentially rely on the same mechanism, although they are given different names: momentum annealing (MA) (26), stochastic cellular automata annealing (SCA) (27), and restricted Boltzmann machine (RBM)'s parallel stochastic sampling (28).…”

Section: Introductionmentioning

confidence: 99%

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High-performance combinatorial optimization based on classical mechanics

et al. 2021

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Quickly obtaining optimal solutions of combinatorial optimization problems has tremendous value but is extremely difficult. Thus, various kinds of machines specially designed for combinatorial optimization have recently been proposed and developed. Toward the realization of higher-performance machines, here, we propose an algorithm based on classical mechanics, which is obtained by modifying a previously proposed algorithm called simulated bifurcation. Our proposed algorithm allows us to achieve not only high speed by parallel computing but also high solution accuracy for problems with up to one million binary variables. Benchmarking shows that our machine based on the algorithm achieves high performance compared to recently developed machines, including a quantum annealer using a superconducting circuit, a coherent Ising machine using a laser, and digital processors based on various algorithms. Thus, high-performance combinatorial optimization is realized by massively parallel implementations of the proposed algorithm based on classical mechanics.

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Section: Performance For a 2000-spin Ising Problem With All-to-all Connectivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-performance combinatorial optimization based on classical mechanics

et al. 2021

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“…Ising machines, which are recently expected to solve combinatorial optimization problems at high speed, have been developed [4]- [10] and utilized in many fields. A combinatorial optimization problem is to search for the optimal combination of decision variables so as to maximize or minimize the objective function under satisfying the given constraints.…”

Section: Ising Machinesmentioning

confidence: 99%

An Ising Machine-Based Solver for Visiting-Route Recommendation Problems in Amusement Parks

Mukasa

Wakaizumi

Tanaka

et al. 2021

IEICE Trans. Inf. & Syst.

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In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.

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“…Sparked by the work of D-Wave Systems Inc. [1,2,3], a whole new field of optimization based on physical processes has emerged. Some examples are: digital processors based on simulated annealing [4,5,6,7] or other recently proposed algorithms [8,9,10,11]; coherent Ising machines implemented with pulse lasers [12,13,14,15,16,17], and other kinds of optical Ising machine [18,19,20,21]; simulated bifurcation [22,23] and other optimization using nonlinear oscillation networks [24,25,26,27,28]. Specifically, the development of hardware quantum annealers has stimulated new ways of tackling NPhard problems previously inaccessible.…”

Section: Introductionmentioning

confidence: 99%

Computational overhead of locality reduction in binary optimization problems

Valiante

Hernandez

Barzegar

et al. 2021

Computer Physics Communications

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STATICA: A 512-Spin 0.25M-Weight Annealing Processor With an All-Spin-Updates-at-Once Architecture for Combinatorial Optimization With Complete Spin–Spin Interactions

Cited by 76 publications

References 19 publications

High-performance combinatorial optimization based on classical mechanics

High-performance combinatorial optimization based on classical mechanics

An Ising Machine-Based Solver for Visiting-Route Recommendation Problems in Amusement Parks

Computational overhead of locality reduction in binary optimization problems

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