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2020 – today
- 2024
[i14]Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru, Tayfun Gokmen, Chai Wah Wu, Anshul Gupta, Haim Avron, Tomasz Nowicki, Malte J. Rasch, O. Murat Onen, Vanessa López-Marrero
, Effendi Leobandung, Yasuteru Kohda, Wilfried Haensch, Lior Horesh:
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR abs/2401.13754 (2024)- [i13]Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen:
Towards Exact Gradient-based Training on Analog In-memory Computing. CoRR abs/2406.12774 (2024) - 2023
- [c6]Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu, Anshul Gupta, Shashanka Ubaru, Tayfun Gokmen, Lior Horesh:
Solving Sparse Linear Systems via Flexible GMRES with In-Memory Analog Preconditioning. HPEC 2023: 1-7 - [i12]Malte J. Rasch, Fabio Carta, Omobayode Fagbohungbe, Tayfun Gokmen:
Fast offset corrected in-memory training. CoRR abs/2303.04721 (2023) - 2022
- [j5]O. Murat Onen, Tayfun Gokmen, Teodor K. Todorov, Tomasz Nowicki, Jesús A. del Alamo, John Rozen, Wilfried Haensch, Seyoung Kim:
Neural Network Training With Asymmetric Crosspoint Elements. Frontiers Artif. Intell. 5: 891624 (2022) - [i11]O. Murat Onen, Tayfun Gokmen, Teodor K. Todorov, Tomasz Nowicki, Jesús A. del Alamo, John Rozen, Wilfried Haensch, Seyoung Kim:
Neural Network Training with Asymmetric Crosspoint Elements. CoRR abs/2201.13377 (2022) - 2021
- [j4]Tayfun Gokmen:
Enabling Training of Neural Networks on Noisy Hardware. Frontiers Artif. Intell. 4: 699148 (2021) - [c5]Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan:
A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays. AICAS 2021: 1-4 - [c4]Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu, Tayfun Gokmen, Haim Avron:
Solving sparse linear systems with approximate inverse preconditioners on analog devices. HPEC 2021: 1-7 - [i10]Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan:
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays. CoRR abs/2104.02184 (2021) - 2020
- [j3]Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch:
Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays. IEEE Des. Test 37(2): 19-29 (2020) - [c3]David Brooks, Martin M. Frank, Tayfun Gokmen, Udit Gupta, Xiaobo Sharon Hu, Shubham Jain, Ann Franchesca Laguna, Michael T. Niemier, Ian O'Connor, Anand Raghunathan, Ashish Ranjan, Dayane Reis, Jacob R. Stevens, Carole-Jean Wu, Xunzhao Yin:
Emerging Neural Workloads and Their Impact on Hardware. DATE 2020: 1462-1471
2010 – 2019
- 2019
- [j2]Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan:
Neural network accelerator design with resistive crossbars: Opportunities and challenges. IBM J. Res. Dev. 63(6): 10:1-10:13 (2019) - [j1]Wilfried Haensch, Tayfun Gokmen, Ruchir Puri:
The Next Generation of Deep Learning Hardware: Analog Computing. Proc. IEEE 107(1): 108-122 (2019) - [c2]Eduard A. Cartier, Wanki Kim, Nanbo Gong, Tayfun Gokmen, Martin M. Frank, Douglas M. Bishop, Youngseok Kim, Seyoung Kim, Takashi Ando, Ernest Y. Wu, Praneet Adusumilli, John Rozen, Paul M. Solomon, Wilfried Haensch, Matthew J. BrightSky, Abu Sebastian, Geoffrey W. Burr, Vijay Narayanan:
Reliability Challenges with Materials for Analog Computing. IRPS 2019: 1-10 - [i9]Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch:
Training large-scale ANNs on simulated resistive crossbar arrays. CoRR abs/1906.02698 (2019) - [i8]O. Murat Onen, Brenden A. Butters, Emily Toomey, Tayfun Gokmen, Karl K. Berggren:
Design and Characterization of Superconducting Nanowire-Based Processors for Acceleration of Deep Neural Network Training. CoRR abs/1907.02886 (2019) - [i7]Hyungjun Kim, Malte J. Rasch, Tayfun Gokmen, Takashi Ando, Hiroyuki Miyazoe, Jae-Joon Kim, John Rozen, Seyoung Kim:
Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays. CoRR abs/1907.10228 (2019) - [i6]Tayfun Gokmen, Wilfried Haensch:
Algorithm for Training Neural Networks on Resistive Device Arrays. CoRR abs/1909.07908 (2019) - 2018
- [i5]Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch:
Training LSTM Networks with Resistive Cross-Point Devices. CoRR abs/1806.00166 (2018) - [i4]Malte J. Rasch, Tayfun Gokmen, Mattia Rigotti, Wilfried Haensch:
Efficient ConvNets for Analog Arrays. CoRR abs/1807.01356 (2018) - 2017
- [c1]Seyoung Kim, Tayfun Gokmen, Hyung-Min Lee, Wilfried E. Haensch:
Analog CMOS-based resistive processing unit for deep neural network training. MWSCAS 2017: 422-425 - [i3]Tayfun Gokmen, O. Murat Onen, Wilfried Haensch:
Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices. CoRR abs/1705.08014 (2017) - [i2]Seyoung Kim, Tayfun Gokmen, Hyung-Min Lee, Wilfried E. Haensch:
Analog CMOS-based Resistive Processing Unit for Deep Neural Network Training. CoRR abs/1706.06620 (2017) - 2016
- [i1]Gökmen Tayfun, Yurii Vlasov:
Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices. CoRR abs/1603.07341 (2016)
Coauthor Index
aka: Wilfried E. Haensch
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last updated on 2024-10-07 21:24 CEST by the dblp team
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