Gast ::
| Hauptseite > Publikationsdatenbank > An efficient synchronous-updating memristor-based Ising solver for combinatorial optimization > print |
| Hauptseite > Publikationsdatenbank > An efficient synchronous-updating memristor-based Ising solver for combinatorial optimization > print |
| 001 | 1021714 | ||
| 005 | 20240403082800.0 | ||
| 024 | 7 | _ | |a 10.1109/IEDM45625.2022.10019348 |2 doi |
| 024 | 7 | _ | |a WOS:000968800700008 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-00959 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Jiang, Mingrui |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a 2022 IEEE International Electron Devices Meeting (IEDM) |g IEDM |c San Francisco |d 2022-12-03 - 2022-12-07 |w USA |
| 245 | _ | _ | |a An efficient synchronous-updating memristor-based Ising solver for combinatorial optimization |
| 260 | _ | _ | |c 2022 |b IEEE |
| 300 | _ | _ | |a 22.2.1-22.2.4 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1710249608_19742 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a 5233 - Memristive Materials and Devices (POF4-523) |0 G:(DE-HGF)POF4-5233 |c POF4-523 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Shan, Keyi |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Sheng, Xia |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Graves, Cat |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Strachan, John Paul |0 P:(DE-Juel1)188145 |b 4 |e Corresponding author |
| 700 | 1 | _ | |a Li, Can |0 P:(DE-HGF)0 |b 5 |
| 773 | _ | _ | |a 10.1109/IEDM45625.2022.10019348 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1021714 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)188145 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5234 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5233 |x 1 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-14-20210412 |k PGI-14 |l Neuromorphic Compute Nodes |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-14-20210412 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|