%0 Conference Paper
%A Jiang, Mingrui
%A Shan, Keyi
%A Sheng, Xia
%A Graves, Cat
%A Strachan, John Paul
%A Li, Can
%T An efficient synchronous-updating memristor-based Ising solver for combinatorial optimization
%I IEEE
%M FZJ-2024-00959
%P 22.2.1-22.2.4
%D 2022
%X Despite 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.
%B 2022 IEEE International Electron Devices Meeting (IEDM)
%C 3 Dec 2022 - 7 Dec 2022, San Francisco (USA)
Y2 3 Dec 2022 - 7 Dec 2022
M2 San Francisco, USA
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U <Go to ISI:>//WOS:000968800700008
%R 10.1109/IEDM45625.2022.10019348
%U https://juser.fz-juelich.de/record/1021714