Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices. - Gökmen Tayfun; Yurii Vlasov (CoRR, 2016)

Analog CMOS-based resistive processing unit for deep neural network training. - Seyoung Kim; Tayfun Gokmen; Hyung-Min Lee; Wilfried E. Haensch (MWSCAS, 2017)

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices. - Tayfun Gokmen; O. Murat Onen; Wilfried Haensch (CoRR, 2017)

Training LSTM Networks with Resistive Cross-Point Devices. - Tayfun Gokmen; Malte J. Rasch; Wilfried Haensch (CoRR, 2018)

Efficient ConvNets for Analog Arrays. - Malte J. Rasch; Tayfun Gokmen; Mattia Rigotti; Wilfried Haensch (CoRR, 2018)

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; et al. (IRPS, 2019)

Training large-scale ANNs on simulated resistive crossbar arrays. - Malte J. Rasch; Tayfun Gokmen; Wilfried Haensch (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)

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)

Algorithm for Training Neural Networks on Resistive Device Arrays. - Tayfun Gokmen; Wilfried Haensch (CoRR, 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 001; Anand Raghunathan (IBM J. Res. Dev., 2019)

The Next Generation of Deep Learning Hardware: Analog Computing. - Wilfried Haensch; Tayfun Gokmen; Ruchir Puri (Proc. IEEE, 2019)

Emerging Neural Workloads and Their Impact on Hardware. - David Brooks 001; 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 001; Dayane Reis; et al. (DATE, 2020)

Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays. - Malte J. Rasch; Tayfun Gokmen; Wilfried Haensch (IEEE Des. Test, 2020)

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 (AICAS, 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 (HPEC, 2021)

Enabling Training of Neural Networks on Noisy Hardware. - Tayfun Gokmen (Frontiers Artif. Intell., 2021)

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)

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 (HPEC, 2023)

Fast offset corrected in-memory training. - Malte J. Rasch; Fabio Carta; Omobayode Fagbohungbe; Tayfun Gokmen (CoRR, 2023)

Towards Exact Gradient-based Training on Analog In-memory Computing. - Zhaoxian Wu; Tayfun Gokmen; Malte J. Rasch; Tianyi Chen (NeurIPS, 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; et al. (CoRR, 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)

NORA: Noise-Optimized Rescaling of LLMs on Analog Compute-in-Memory Accelerators. - Yayue Hou; Hsinyu Tsai; Kaoutar El Maghraoui; Tayfun Gokmen; Geoffrey W. Burr; Liu Liu (DATE, 2025)

Assessing the Performance of Analog Training for Transfer Learning. - Omobayode Fagbohungbe; Corey Lammie; Malte J. Rasch; Takashi Ando; Tayfun Gokmen; Vijay Narayanan (ISCAS, 2025)

Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions. - Zhaoxian Wu; Quan Xiao; Tayfun Gokmen; Omobayode Fagbohungbe; Tianyi Chen (CoRR, 2025)