Sputtering of Metal Oxide Semiconductor n-p Stacks. - Andrea Fasoli; L. Thornquist; K. Lionti; Linda K. Sundberg; L. Bozano (ISOEN, 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)

4-Bit Quantization of LSTM-Based Speech Recognition Models. - Andrea Fasoli; Chia-Yu Chen; Mauricio J. Serrano; Xiao Sun 013; Naigang Wang; Swagath Venkataramani; George Saon; Xiaodong Cui; Brian Kingsbury; Wei Zhang 022; Zoltán Tüske; Kailash Gopalakrishnan (Interspeech, 2021)

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)

Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization. - Andrea Fasoli; Chia-Yu Chen; Mauricio J. Serrano; Swagath Venkataramani; George Saon; Xiaodong Cui; Brian Kingsbury; Kailash Gopalakrishnan (INTERSPEECH, 2022)

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)

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-AI Hardware Accelerators for Low-Latency Transformer-Based Language Models (Invited). - Geoffrey W. Burr; H. Tsai; Irem Boybat; William Andrew Simon; Julian Büchel; Athanasios Vasilopoulos; Pritish Narayanan; Andrea Fasoli; Kohji Hosokawa; Manuel Le Gallo; M. Ishii; Yasuteru Kohda; et al. (CICC, 2025)