Hsinyu Tsai

32 papers A 2C 5Journal 16Unranked 9
YearRankTypeTitle / Venue / Authors
2026 — conf
CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization.
EACL (Industry Track)
Che-Ming Chang, Prashanth Vijayaraghavan, Ashutosh Jadhav, Charles Mackin, Hsinyu Tsai, Vandana V. Mukherjee, Ehsan Degan
2026 J jnl
CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization.
CoRR
Che-Ming Chang, Prashanth Vijayaraghavan, Ashutosh Jadhav, Charles Mackin, Vandana V. Mukherjee, Hsinyu Tsai, Ehsan Degan
2026 J jnl
Robust Heterogeneous Analog-Digital Computing for Mixture-of-Experts Models with Theoretical Generalization Guarantees.
CoRR
Mohammed Nowaz Rabbani Chowdhury, Hsinyu Tsai, Geoffrey W. Burr, Kaoutar El Maghraoui, Liu Liu, Meng Wang
2025 J jnl
CiMBA: Accelerating Genome Sequencing Through On-Device Basecalling via Compute-in-Memory.
IEEE Trans. Parallel Distributed Syst.
William Andrew Simon, Irem Boybat, Riselda Kodra, Elena Ferro, Gagandeep Singh, Mohammed Alser, Shubham Jain, Hsinyu Tsai, Geoffrey W. Burr, Onur Mutlu, Abu Sebastian
2025 J jnl
CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory.
CoRR
William Andrew Simon, Irem Boybat, Riselda Kodra, Elena Ferro, Gagandeep Singh, Mohammed Alser, Shubham Jain, Hsinyu Tsai, Geoffrey W. Burr, Onur Mutlu, Abu Sebastian
2025 J jnl
Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing.
Nat. Comput. Sci.
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
2025 A conf
NORA: Noise-Optimized Rescaling of LLMs on Analog Compute-in-Memory Accelerators.
DATE
Yayue Hou, Hsinyu Tsai, Kaoutar El Maghraoui, Tayfun Gokmen, Geoffrey W. Burr, Liu Liu
2025 — conf
RTLExplain: A Structured Approach to RTL Code Summarization and Question Answering for Medium-to-Large Designs Using LLMs.
MLCAD
Ting-Hsun Chi, Charles Mackin, Luyao Shi, Prashanth Vijayaraghavan, Hsinyu Tsai, Ehsan Degan
2025 J jnl
Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing.
CoRR
Jose Luquin, Charles Mackin, Stefano Ambrogio, An Chen, F. Baldi, G. Miralles, Malte J. Rasch, Julian Büchel, M. Lalwani, W. Ponghiran, Paul Solomon, Hsinyu Tsai, Geoffrey W. Burr, Pritish Narayanan
2025 A conf
SAGE: Saliency-Aware Grouping for Efficient Mapping of LLMs on Analog Compute-in-Memory.
ICCAD
Yayue Hou, Zhenyu Liu, Garrett Gagnon, Hsinyu Tsai, Kaoutar El Maghraoui, Geoffrey W. Burr, Liu Liu
2024 J jnl
Pipeline Gradient-based Model Training on Analog In-memory Accelerators.
CoRR
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Hsinyu Tsai, Kaoutar El Maghraoui, Tianyi Chen
2023 J jnl
A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh.
IEEE Trans. Very Large Scale Integr. Syst.
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, Arvind Kumar, Vijay Narayanan, Geoffrey W. Burr
2023 J jnl
An analog-AI chip for energy-efficient speech recognition and transcription.
Nat.
Stefano Ambrogio, Pritish Narayanan, Atsuya Okazaki, Andrea Fasoli, Charles Mackin, Kohji Hosokawa, Akiyo Nomura, Takeo Yasuda, An Chen, Alexander M. Friz, Masatoshi Ishii, Jose Luquin, Yasuteru Kohda, Nicole Saulnier, Kevin Brew, Samuel Choi, Injo Ok, Timothy Philip, Victor Chan, Mary Claire Silvestre, Ishtiaq Ahsan, Vijay Narayanan, Hsinyu Tsai, Geoffrey W. Burr
2023 — conf
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing.
EDGE
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
2023 J jnl
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing.
CoRR
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
2023 C conf
Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited).
ISCAS
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, Martin M. Frank, Takeo Yasuda, Alexander M. Friz, Yasuteru Kohda, An Chen, Andrea Fasoli, Malte J. Rasch, Stanislaw Wozniak, Jose Luquin, Vijay Narayanan, Geoffrey W. Burr
2023 J jnl
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.
CoRR
Malte J. Rasch, Charles Mackin, Manuel Le Gallo, An Chen, Andrea Fasoli, Frédéric Odermatt, Ning Li, S. R. Nandakumar, Pritish Narayanan, Hsinyu Tsai, Geoffrey W. Burr, Abu Sebastian, Vijay Narayanan
2023 — conf
Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited).
IRPS
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, Nicole Saulnier, Irem Boybat, Stanislaw Wozniak, Abu Sebastian, Pritish Narayanan, Charles Mackin, An Chen, Hsinyu Tsai, Geoffrey W. Burr
2023 — conf
Phase Change Memory-based Hardware Accelerators for Deep Neural Networks (invited).
VLSI Technology and Circuits
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, Samuel Choi, T. Gordon, T. M. Levin, Alexander M. Friz, Masatoshi Ishii, Yasuteru Kohda, An Chen, Andrea Fasoli, Jose Luquin, Nicole Saulnier, S. Teehan, Ishtiaq Ahsan, Vijay Narayanan
2023 J jnl
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference.
CoRR
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
2022 C conf
Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers.
ISCAS
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, An Chen, Andrea Fasoli, Malte J. Rasch, Geoffrey W. Burr
2021 C conf
Circuit Techniques for Efficient Acceleration of Deep Neural Network Inference with Analog-AI (Invited).
ISCAS
Kohji Hosokawa, Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, Charles Mackin, Andrea Fasoli, Alexander M. Friz, An Chen, Jose Luquin, Katherine Spoon, Geoffrey W. Burr, Scott C. Lewis
2021 — conf
Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation.
IRPS
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, Praneet Adusumilli, Katie Spoon, Stefano Ambrogio, Benedikt Kersting, Thomas Bohnstingl, Manuel Le Gallo, Andrew Simon, Ning Li, Iqbal Saraf, Jin-Ping Han, Lynne M. Gignac, John M. Papalia, Tenko Yamashita, Nicole Saulnier, Geoffrey W. Burr, Hsinyu Tsai, Abu Sebastian, Vijay Narayanan, Matthew BrightSky
2021 J jnl
Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices.
Frontiers Comput. Neurosci.
Katie Spoon, Hsinyu Tsai, An Chen, Malte J. Rasch, Stefano Ambrogio, Charles Mackin, Andrea Fasoli, Alexander M. Friz, Pritish Narayanan, Milos Stanisavljevic, Geoffrey W. Burr
2020 — conf
Accelerating Deep Neural Networks with Analog Memory Devices.
AICAS
Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Charles Mackin, Katherine Spoon, An Chen, Andrea Fasoli, Alexander M. Friz, Geoffrey W. Burr
2020 — conf
Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges.
IRPS
Charles Mackin, Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, Katie Spoon, Andrea Fasoli, An Chen, Alexander M. Friz, Robert M. Shelby, Geoffrey W. Burr
2020 C conf
Optimization of Analog Accelerators for Deep Neural Networks Inference.
ISCAS
Andrea Fasoli, Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Charles Mackin, Katherine Spoon, Alexander M. Friz, An Chen, Geoffrey W. Burr
2019 J jnl
AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed.
IBM J. Res. Dev.
Hung-Yang Chang, Pritish Narayanan, Scott C. Lewis, Nathan C. P. Farinha, Kohji Hosokawa, Charles Mackin, Hsinyu Tsai, Stefano Ambrogio, An Chen, Geoffrey W. Burr
2019 J jnl
Analog-to-Digital Conversion With Reconfigurable Function Mapping for Neural Networks Activation Function Acceleration.
IEEE J. Emerg. Sel. Topics Circuits Syst.
Massimo Giordano, Giorgio Cristiano, Koji Ishibashi, Stefano Ambrogio, Hsinyu Tsai, Geoffrey W. Burr, Pritish Narayanan
2018 J jnl
Equivalent-accuracy accelerated neural-network training using analogue memory.
Nat.
Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Robert M. Shelby, Irem Boybat, Carmelo di Nolfo, Severin Sidler, Massimo Giordano, Martina Bodini, Nathan C. P. Farinha, Benjamin Killeen, Christina Cheng, Yassine Jaoudi, Geoffrey W. Burr
2017 — conf
Improved Deep Neural Network Hardware-Accelerators Based on Non-Volatile-Memory: The Local Gains Technique.
ICRC
Irem Boybat, Carmelo di Nolfo, Stefano Ambrogio, Martina Bodini, Nathan C. P. Farinha, Robert M. Shelby, Pritish Narayanan, Severin Sidler, Hsinyu Tsai, Yusuf Leblebici, Geoffrey W. Burr
2017 C conf
Neuromorphic devices and architectures for next-generation cognitive computing.
ISCAS
Geoffrey W. Burr, Pritish Narayanan, Robert M. Shelby, Stefano Ambrogio, Hsinyu Tsai, Scott L. Lewis, Kohji Hosokawa