Gast ::
| Hauptseite > Publikationsdatenbank > The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming > print |
| Hauptseite > Publikationsdatenbank > The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming > print |
| 001 | 1038036 | ||
| 005 | 20250203103305.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2403.06712 |2 doi |
| 024 | 7 | _ | |a 10.48550/arXiv.2403.06712 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-01085 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-01085 |
| 100 | 1 | _ | |a Yu, Zhenming |0 P:(DE-Juel1)190500 |b 0 |u fzj |
| 245 | _ | _ | |a The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming |
| 260 | _ | _ | |c 2024 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1738239409_31383 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time. However, on-chip training with memristor arrays still faces challenges, including device-to-device and cycle-to-cycle variations, switching non-linearity, and especially SET and RESET asymmetry. To combat device non-linearity and asymmetry, we propose to program memristors by harnessing neural networks that map desired conductance updates to the required pulse times. With our method, approximately 95% of devices can be programmed within a relative percentage difference of +-50% from the target conductance after just one attempt. Our approach substantially reduces memristor programming delays compared to traditional write-and-verify methods, presenting an advantageous solution for on-chip training scenarios. Furthermore, our proposed neural network can be accelerated by memristor arrays upon deployment, providing assistance while reducing hardware overhead compared with previous works. This work contributes significantly to the practical application of memristors, particularly in reducing delays in memristor programming. It also envisions the future development of memristor-based machine learning accelerators. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a BMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (16ME0400) |0 G:(BMBF)16ME0400 |c 16ME0400 |x 1 |
| 536 | _ | _ | |a BMBF 03ZU1106CA - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - A (03ZU1106CA) |0 G:(BMBF)03ZU1106CA |c 03ZU1106CA |x 2 |
| 536 | _ | _ | |a BMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB) |0 G:(DE-Juel1)BMBF-03ZU1106CB |c BMBF-03ZU1106CB |x 3 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Emerging Technologies (cs.ET) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Yang, Ming-Jay |0 P:(DE-Juel1)192385 |b 1 |u fzj |
| 700 | 1 | _ | |a Finkbeiner, Jan |0 P:(DE-Juel1)190112 |b 2 |u fzj |
| 700 | 1 | _ | |a Siegel, Sebastian |0 P:(DE-Juel1)174486 |b 3 |u fzj |
| 700 | 1 | _ | |a Strachan, John Paul |0 P:(DE-Juel1)188145 |b 4 |u fzj |
| 700 | 1 | _ | |a Neftci, Emre |0 P:(DE-Juel1)188273 |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.48550/arXiv.2403.06712 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2403.06712 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1038036/files/2403.06712v1.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1038036 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)190500 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)192385 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)190112 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)174486 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)188145 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)188273 |
| 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 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-15-20210701 |k PGI-15 |l Neuromorphic Software Eco System |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-14-20210412 |k PGI-14 |l Neuromorphic Compute Nodes |x 1 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-15-20210701 |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-14-20210412 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|