AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed. - Hung-Yang Chang; Pritish Narayanan; Scott C. Lewis; Nathan C. P. Farinha; Kohji Hosokawa; Charles Mackin; Hsinyu Tsai; Stefano Ambrogio; An Chen 002; Geoffrey W. Burr (IBM J. Res. Dev., 2019)

Accelerating Deep Neural Networks with Analog Memory Devices. - Stefano Ambrogio; Pritish Narayanan; Hsinyu Tsai; Charles Mackin; Katherine Spoon; An Chen 002; Andrea Fasoli; Alexander M. Friz; Geoffrey W. Burr (AICAS, 2020)

Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges. - Charles Mackin; Pritish Narayanan; Stefano Ambrogio; Hsinyu Tsai; Katie Spoon; Andrea Fasoli; An Chen 002; Alexander M. Friz; Robert M. Shelby; Geoffrey W. Burr (IRPS, 2020)

Optimization of Analog Accelerators for Deep Neural Networks Inference. - Andrea Fasoli; Stefano Ambrogio; Pritish Narayanan; Hsinyu Tsai; Charles Mackin; Katherine Spoon; Alexander M. Friz; An Chen 002; Geoffrey W. Burr (ISCAS, 2020)

Circuit Techniques for Efficient Acceleration of Deep Neural Network Inference with Analog-AI (Invited). - Kohji Hosokawa; Pritish Narayanan; Stefano Ambrogio; Hsinyu Tsai; Charles Mackin; Andrea Fasoli; Alexander M. Friz; An Chen 002; Jose Luquin; Katherine Spoon; Geoffrey W. Burr; Scott C. Lewis (ISCAS, 2021)

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. - Katie Spoon; Hsinyu Tsai; An Chen 002; Malte J. Rasch; Stefano Ambrogio; Charles Mackin; Andrea Fasoli; Alexander M. Friz; Pritish Narayanan; Milos Stanisavljevic; Geoffrey W. Burr (Frontiers Comput. Neurosci., 2021)

Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers. - Atsuya Okazaki; Pritish Narayanan; Stefano Ambrogio; Kohji Hosokawa; Hsinyu Tsai; Akiyo Nomura; Takeo Yasuda; Charles Mackin; Alexander M. Friz; Masatoshi Ishii; Yasuteru Kohda; Katie Spoon; et al. (ISCAS, 2022)

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)

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)

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)

Analog Foundation Models. - Julian Büchel; Iason Chalas; Giovanni Acampa; An Chen 002; Omobayode Fagbohungbe; Sidney Tsai; Kaoutar El Maghraoui; Manuel Le Gallo; Abbas Rahimi; Abu Sebastian (CoRR, 2025)

Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing. - Jose Luquin; Charles Mackin; Stefano Ambrogio; An Chen 002; F. Baldi; G. Miralles; Malte J. Rasch; Julian Büchel; M. Lalwani; W. Ponghiran; Paul Solomon; Hsinyu Tsai; et al. (CoRR, 2025)