Tayfun Gokmen

According to our database, Tayfun Gokmen authored at least 23 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Pipeline Gradient-based Model Training on Analog In-memory Accelerators.

Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Hsinyu Tsai, Kaoutar El Maghraoui, Tianyi Chen

CoRR, 2024

Towards Exact Gradient-based Training on Analog In-memory Computing.

Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen

CoRR, 2024

Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation.

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

CoRR, 2024

2023

Fast offset corrected in-memory training.

Malte J. Rasch, Fabio Carta, Omobayode Fagbohungbe, Tayfun Gokmen

CoRR, 2023

Solving Sparse Linear Systems via Flexible GMRES with In-Memory Analog Preconditioning.

Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu, Anshul Gupta, Shashanka Ubaru, Tayfun Gokmen, Lior Horesh

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

2022

Neural Network Training With Asymmetric Crosspoint Elements.

O. Murat Onen, Tayfun Gokmen, Teodor K. Todorov, Tomasz Nowicki, Jesús A. del Alamo, John Rozen, Wilfried Haensch, Seyoung Kim

Frontiers Artif. Intell., 2022

2021

Enabling Training of Neural Networks on Noisy Hardware.

Tayfun Gokmen

Frontiers Artif. Intell., 2021

Solving sparse linear systems with approximate inverse preconditioners on analog devices.

Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu, Tayfun Gokmen, Haim Avron

Proceedings of the 2021 IEEE High Performance Extreme Computing Conference, 2021

A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays.

Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan

Proceedings of the 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2021

2020

Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays.

Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch

IEEE Des. Test, 2020

Emerging Neural Workloads and Their Impact on Hardware.

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

Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

2019

The Next Generation of Deep Learning Hardware: Analog Computing.

Wilfried Haensch, Tayfun Gokmen, Ruchir Puri

Proc. IEEE, 2019

Neural network accelerator design with resistive crossbars: Opportunities and challenges.

Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan

IBM J. Res. Dev., 2019

Algorithm for Training Neural Networks on Resistive Device Arrays.

Tayfun Gokmen, Wilfried Haensch

CoRR, 2019

Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays.

Hyungjun Kim, Malte J. Rasch, Tayfun Gokmen, Takashi Ando, Hiroyuki Miyazoe, Jae-Joon Kim, John Rozen, Seyoung Kim

CoRR, 2019

Design and Characterization of Superconducting Nanowire-Based Processors for Acceleration of Deep Neural Network Training.

O. Murat Onen, Brenden A. Butters, Emily Toomey, Tayfun Gokmen, Karl K. Berggren

CoRR, 2019

Training large-scale ANNs on simulated resistive crossbar arrays.

Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch

CoRR, 2019

Reliability Challenges with Materials for Analog Computing.

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

Proceedings of the IEEE International Reliability Physics Symposium, 2019

2018

Efficient ConvNets for Analog Arrays.

Malte J. Rasch, Tayfun Gokmen, Mattia Rigotti, Wilfried Haensch

CoRR, 2018

Training LSTM Networks with Resistive Cross-Point Devices.

Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch

CoRR, 2018

2017

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Tayfun Gokmen, O. Murat Onen, Wilfried Haensch

CoRR, 2017

Analog CMOS-based resistive processing unit for deep neural network training.

Seyoung Kim, Tayfun Gokmen, Hyung-Min Lee, Wilfried E. Haensch

Proceedings of the IEEE 60th International Midwest Symposium on Circuits and Systems, 2017

2016

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices.

Gökmen Tayfun, Yurii Vlasov

CoRR, 2016