A Simplified Yield Model for SRAM Repair in Advanced Technology. - Xiaoyuan Qi; Raymond J. Rosner; John Hopkins; Jack M. Higman; Rick Mewhirter; Aaron Sinnott; Binod Kumar G. Nair; Ishtiaq Ahsan; Mark Lagus (IEEE Trans. Very Large Scale Integr. Syst., 2018)

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)

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)

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)

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)