Advanced high-k dielectric stacks with polySi and metal gates: Recent progress and current challenges. - Evgeni P. Gusev; Vijay Narayanan; Martin M. Frank (IBM J. Res. Dev., 2006)

3D GPU architecture using cache stacking: Performance, cost, power and thermal analysis. - Ahmed Al-Maashri; Guangyu Sun 003; Xiangyu Dong 001; Vijay Narayanan; Yuan Xie 001 (ICCD, 2009)

Characterizing the soft error vulnerability of multicores running multithreaded applications. - Niranjan Soundararajan; Anand Sivasubramaniam; Vijay Narayanan (SIGMETRICS, 2010)

A Quantitative Evaluation Framework for Missing Value Imputation Algorithms. - Vinod Nair; Rahul Kidambi; Sundararajan Sellamanickam; S. Sathiya Keerthi; Johannes Gehrke; Vijay Narayanan (CoRR, 2013)

High-mobility high-Ge-content Si1-xGex-OI PMOS FinFETs with fins formed using 3D germanium condensation with Ge fraction up to x∼ 0.7, scaled EOT∼8.5Å and ∼10nm fin width. - Pouya Hashemi; Takashi Ando; Karthik Balakrishnan; John Bruley; Sebastian U. Engelmann; John A. Ott; Vijay Narayanan; D.-G. Park; Renee T. Mo; Effendi Leobandung (VLSIC, 2015)

Interface engineering of Si1-xGex gate stacks for high performance dual channel CMOS. - ChoongHyun Lee; Richard G. Southwick; Shogo Mochizuki; Paul Jamison; Ruqiang Bao; Rajan Kumar Pandey; Aniruddha Konar; Takashi Ando; Vijay Narayanan; Bala Haran; Hemanth Jagannathan (ASICON, 2017)

Electron mobility in thin In0.53Ga0.47As channel. - Eduard Cartier; Atreya Majumdar; K.-T. Lee; Takashi Ando; Martin M. Frank; John Rozen; Keith A. Jenkins; C. Liang; C.-W. Cheng; John Bruley; M. Hopstaken; Pranita Kerber; et al. (ESSDERC, 2017)

PBTI in InGaAs MOS capacitors with Al2O3/HfO2/TiN gate stacks: Interface-state generation. - Eduard Cartier; Martin M. Frank; Takashi Ando; John Rozen; Vijay Narayanan (IRPS, 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)

Robust Coreset Construction for Distributed Machine Learning. - Hanlin Lu; Ming-Ju Li; Ting He 001; Shiqiang Wang 001; Vijay Narayanan; Kevin S. Chan (CoRR, 2019)

Nanotechnology-inspired Information Processing Systems of the Future. - Randy Bryant; Mark D. Hill; Tom Kazior; Daniel Lee; Jie Liu; Klara Nahrstedt; Vijay Narayanan; Jan M. Rabaey; Hava T. Siegelmann; Naresh R. Shanbhag; Naveen Verma; H.-S. Philip Wong (CoRR, 2020)

Hardware and Software Co-optimization for the Initialization Failure of the ReRAM-based Cross-bar Array. - Youngseok Kim; Seyoung Kim; Chun-Chen Yeh; Vijay Narayanan; Jungwook Choi (ACM J. Emerg. Technol. Comput. Syst., 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)

Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation. - Robert L. Bruce; Syed Ghazi Sarwat; Irem Boybat; Cheng-Wei Cheng; Wanki Kim; S. R. Nandakumar; Charles Mackin; Timothy Philip; Zuoguang Liu; Kevin Brew; Nanbo Gong; Injo Ok; et al. (IRPS, 2021)

HERMES Core - A 14nm CMOS and PCM-based In-Memory Compute Core using an array of 300ps/LSB Linearized CCO-based ADCs and local digital processing. - Riduan Khaddam-Aljameh; Milos Stanisavljevic; Jordi Fornt Mas; Geethan Karunaratne; Matthias Braendli; Femg Liu; Abhairaj Singh; Silvia M. Müller; Urs Egger; Anastasios Petropoulos; Theodore Antonakopoulos 001; Kevin Brew; et al. (VLSI Circuits, 2021)

In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory. - Geethan Karunaratne; Michael Hersche; Jovin Langenegger; Giovanni Cherubini; Manuel Le Gallo; Urs Egger; Kevin Brew; Samuel Choi; Injo Ok; Mary Claire Silvestre; Ning Li; Nicole Saulnier; et al. (ESSCIRC, 2022)

Impact of Phase-Change Memory Flicker Noise and Weight Drift on Analog Hardware Inference for Large-Scale Deep Learning Networks. - Jin-Ping Han; Malte J. Rasch; Zuoguang Liu; Paul Solomon; Kevin Brew; Kangguo Cheng; Injo Ok; Victor Chan; Michael Longstreet; Wanki Kim; Robert L. Bruce; Cheng-Wei Cheng; et al. (Adv. Intell. Syst., 2022)

Joint Coreset Construction and Quantization for Distributed Machine Learning. - Hanlin Lu; Changchang Liu; Shiqiang Wang 001; Ting He 001; Vijay Narayanan; Kevin S. Chan; Stephen Pasteris (CoRR, 2022)

A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. - Manuel Le Gallo; Riduan Khaddam-Aljameh; Milos Stanisavljevic; Athanasios Vasilopoulos; Benedikt Kersting; Martino Dazzi; Geethan Karunaratne; Matthias Braendli; Abhairaj Singh; Silvia M. Mueller; Julian Büchel; Xavier Timoneda Comas; et al. (CoRR, 2022)

HERMES-Core - A 1.59-TOPS/mm2 PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs. - Riduan Khaddam-Aljameh; Milos Stanisavljevic; Jordi Fornt Mas; Geethan Karunaratne; Matthias Brändli; Feng Liu; Abhairaj Singh; Silvia M. Müller; Urs Egger; Anastasios Petropoulos; Theodore Antonakopoulos 001; Kevin Brew; et al. (IEEE J. Solid State Circuits, 2022)

AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. - Hadjer Benmeziane; Corey Lammie; Irem Boybat; Malte J. Rasch; Manuel Le Gallo; Hsinyu Tsai; Ramachandran Muralidhar; Smaïl Niar; Hamza Ouarnoughi; Vijay Narayanan; Abu Sebastian; Kaoutar El Maghraoui (EDGE, 2023)

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited). - Martin M. Frank; Ning Li; Malte J. Rasch; Shubham Jain; Ching-Tzu Chen; Ramachandran Muralidhar; Jin-Ping Han; Vijay Narayanan; Timothy Philip; Kevin Brew; Andrew Simon; Iqbal Saraf; et al. (IRPS, 2023)

Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited). - Hsinyu Tsai; Pritish Narayanan; Shubham Jain; Stefano Ambrogio; Kohji Hosokawa; Masatoshi Ishii; Charles Mackin; Ching-Tzu Chen; Atsuya Okazaki; Akiyo Nomura; Irem Boybat; Ramachandran Muralidhar; et al. (ISCAS, 2023)

InsectEye: An Intelligent Trap for Insect Biodiversity Monitoring. - Eric Homan; Codey Mathis; Chonghan Lee; Harland M. Patch; Christina M. Grozinger; Vijay Narayanan (VLSID, 2023)

Phase Change Memory-based Hardware Accelerators for Deep Neural Networks (invited). - Geoffrey W. Burr; Pritish Narayanan; Stefano Ambrogio; Atsuya Okazaki; Hsinyu Tsai; Kohji Hosokawa; Charles Mackin; Akiyo Nomura; Takeo Yasuda; J. Demarest; Kevin Brew; Victor Chan; et al. (VLSI Technology and Circuits, 2023)

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. - Malte J. Rasch; Charles Mackin; Manuel Le Gallo; An Chen 002; Andrea Fasoli; Frédéric Odermatt; Ning Li; S. R. Nandakumar; Pritish Narayanan; Hsinyu Tsai; Geoffrey W. Burr; Abu Sebastian; et al. (CoRR, 2023)

Gradient descent-based programming of analog in-memory computing cores. - Julian Büchel; Athanasios Vasilopoulos; Benedikt Kersting; Frédéric Odermatt; Kevin Brew; Injo Ok; Samuel Choi; Iqbal Saraf; Victor Chan; Timothy Philip; Nicole Saulnier; Vijay Narayanan; et al. (CoRR, 2023)

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. - Manuel Le Gallo; Corey Lammie; Julian Büchel; Fabio Carta; Omobayode Fagbohungbe; Charles Mackin; Hsinyu Tsai; Vijay Narayanan; Abu Sebastian; Kaoutar El Maghraoui; Malte J. Rasch (CoRR, 2023)

An analog-AI chip for energy-efficient speech recognition and transcription. - Stefano Ambrogio; Pritish Narayanan; Atsuya Okazaki; Andrea Fasoli; Charles Mackin; Kohji Hosokawa; Akiyo Nomura; Takeo Yasuda; An Chen 002; Alexander M. Friz; Masatoshi Ishii; Jose Luquin; et al. (Nat., 2023)

A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh. - Shubham Jain; Hsinyu Tsai; Ching-Tzu Chen; Ramachandran Muralidhar; Irem Boybat; Martin M. Frank; Stanislaw Wozniak; Milos Stanisavljevic; Praneet Adusumilli; Pritish Narayanan; Kohji Hosokawa; Masatoshi Ishii; et al. (IEEE Trans. Very Large Scale Integr. Syst., 2023)

Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error. - Julian Büchel; Athanasios Vasilopoulos; Benedikt Kersting; Corey Lammie; Kevin Brew; Timothy Philip; Nicole Saulnier; Vijay Narayanan; Manuel Le Gallo; Abu Sebastian (IEEE J. Emerg. Sel. Topics Circuits Syst., 2023)

Analog AI as a Service: A Cloud Platform for In-Memory Computing. - Kaoutar El Maghraoui; Kim Tran; Kurtis Ruby; Borja Godoy; Jordan Murray; Manuel Le Gallo-Bourdeau; Todd Deshane; Pablo Gonzalez; Diego Moreda; Hadjer Benmeziane; Corey Liam Lammie; Julian Büchel; et al. (SSE, 2024)

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

Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing. - Julian Büchel; Athanasios Vasilopoulos; William Andrew Simon; Irem Boybat; Hsinyu Tsai; Geoffrey W. Burr; Hernan Castro; Bill Filipiak; Manuel Le Gallo; Abbas Rahimi; Vijay Narayanan; Abu Sebastian (Nat. Comput. Sci., 2025)