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001021714 0247_ $$2doi$$a10.1109/IEDM45625.2022.10019348
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001021714 037__ $$aFZJ-2024-00959
001021714 041__ $$aEnglish
001021714 1001_ $$0P:(DE-HGF)0$$aJiang, Mingrui$$b0
001021714 1112_ $$a2022 IEEE International Electron Devices Meeting (IEDM)$$cSan Francisco$$d2022-12-03 - 2022-12-07$$gIEDM$$wUSA
001021714 245__ $$aAn efficient synchronous-updating memristor-based Ising solver for combinatorial optimization
001021714 260__ $$bIEEE$$c2022
001021714 300__ $$a22.2.1-22.2.4
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001021714 520__ $$aDespite showing significant potential in solving combinatorial optimization problems, existing memristor-based solvers update node states asynchronously by performing matrix multiplication column-by-column, leaving the massive parallelism of the crossbar not fully exploited. In this work, we propose and experimentally demonstrate solving the optimization problems with a synchronous-updating memristor-based Ising solver, which is realized by a binary neural network-inspired updating algorithm and a physics-inspired annealing method. The newly proposed method saves more than 5x time and 35x energy consumption compared to the state-of-the-art mem-HNN for finding the optimal solution to a 60-node Max-cut problem.
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001021714 536__ $$0G:(DE-HGF)POF4-5233$$a5233 - Memristive Materials and Devices (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001021714 588__ $$aDataset connected to CrossRef Conference
001021714 7001_ $$0P:(DE-HGF)0$$aShan, Keyi$$b1
001021714 7001_ $$0P:(DE-HGF)0$$aSheng, Xia$$b2
001021714 7001_ $$0P:(DE-HGF)0$$aGraves, Cat$$b3
001021714 7001_ $$0P:(DE-Juel1)188145$$aStrachan, John Paul$$b4$$eCorresponding author
001021714 7001_ $$0P:(DE-HGF)0$$aLi, Can$$b5
001021714 773__ $$a10.1109/IEDM45625.2022.10019348
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