TY  - CONF
AU  - Jiang, Mingrui
AU  - Shan, Keyi
AU  - Sheng, Xia
AU  - Graves, Cat
AU  - Strachan, John Paul
AU  - Li, Can
TI  - An efficient synchronous-updating memristor-based Ising solver for combinatorial optimization
PB  - IEEE
M1  - FZJ-2024-00959
SP  - 22.2.1-22.2.4
PY  - 2022
AB  - 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.
T2  - 2022 IEEE International Electron Devices Meeting (IEDM)
CY  - 3 Dec 2022 - 7 Dec 2022, San Francisco (USA)
Y2  - 3 Dec 2022 - 7 Dec 2022
M2  - San Francisco, USA
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:000968800700008
DO  - DOI:10.1109/IEDM45625.2022.10019348
UR  - https://juser.fz-juelich.de/record/1021714
ER  -