Hsinyu Tsai

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 21

Top Topics

Activation Function

Gigabit Ethernet

Top Venues

Publications

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
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. CoRR (2023)
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
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. CoRR (2023)
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
An analog-AI chip for energy-efficient speech recognition and transcription. Nat. 620 (7975) (2023)
Geoffrey W. Burr, Pritish Narayanan, Stefano Ambrogio, Atsuya Okazaki, Hsinyu Tsai, Kohji Hosokawa, Charles Mackin, Akiyo Nomura, Takeo Yasuda, J. Demarest, Kevin Brew, V. Chan, S. 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
Phase Change Memory-based Hardware Accelerators for Deep Neural Networks (invited). VLSI Technology and Circuits (2023)
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
Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited). IRPS (2023)
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
Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited). ISCAS (2023)
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
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. CoRR (2023)
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
A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh. IEEE Trans. Very Large Scale Integr. Syst. 31 (1) (2023)
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
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. EDGE (2023)
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
Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers. ISCAS (2022)
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
Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation. IRPS (2021)
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
Circuit Techniques for Efficient Acceleration of Deep Neural Network Inference with Analog-AI (Invited). ISCAS (2021)
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
Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. Frontiers Comput. Neurosci. 15 (2021)
Andrea Fasoli, Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Charles Mackin, Katherine Spoon, Alexander M. Friz, An Chen, Geoffrey W. Burr
Optimization of Analog Accelerators for Deep Neural Networks Inference. ISCAS (2020)
Charles Mackin, Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, Katie Spoon, Andrea Fasoli, An Chen, Alexander M. Friz, Robert M. Shelby, Geoffrey W. Burr
Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges. IRPS (2020)
Stefano Ambrogio, Pritish Narayanan, Hsinyu Tsai, Charles Mackin, Katherine Spoon, An Chen, Andrea Fasoli, Alexander M. Friz, Geoffrey W. Burr
Accelerating Deep Neural Networks with Analog Memory Devices. AICAS (2020)
Massimo Giordano, Giorgio Cristiano, Koji Ishibashi, Stefano Ambrogio, Hsinyu Tsai, Geoffrey W. Burr, Pritish Narayanan
Analog-to-Digital Conversion With Reconfigurable Function Mapping for Neural Networks Activation Function Acceleration. IEEE J. Emerg. Sel. Topics Circuits Syst. 9 (2) (2019)
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
AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed. IBM J. Res. Dev. 63 (6) (2019)
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
Equivalent-accuracy accelerated neural-network training using analogue memory. Nat. 558 (7708) (2018)
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
Improved Deep Neural Network Hardware-Accelerators Based on Non-Volatile-Memory: The Local Gains Technique. ICRC (2017)
Geoffrey W. Burr, Pritish Narayanan, Robert M. Shelby, Stefano Ambrogio, Hsinyu Tsai, Scott L. Lewis, Kohji Hosokawa
Neuromorphic devices and architectures for next-generation cognitive computing. ISCAS (2017)