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)

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)