Hsinyu Tsai

4.0k total citations · 1 hit paper
89 papers, 2.8k citations indexed

About

Hsinyu Tsai is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Hsinyu Tsai has authored 89 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Electrical and Electronic Engineering, 30 papers in Materials Chemistry and 18 papers in Artificial Intelligence. Recurrent topics in Hsinyu Tsai's work include Ferroelectric and Negative Capacitance Devices (36 papers), Advanced Memory and Neural Computing (35 papers) and Advancements in Photolithography Techniques (14 papers). Hsinyu Tsai is often cited by papers focused on Ferroelectric and Negative Capacitance Devices (36 papers), Advanced Memory and Neural Computing (35 papers) and Advancements in Photolithography Techniques (14 papers). Hsinyu Tsai collaborates with scholars based in United States, Taiwan and Japan. Hsinyu Tsai's co-authors include Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, R. M. Shelby, Ru‐Shi Liu, Zhen Bao, An‐Cih Tang, Hung‐Chia Wang, Massimo Giordano and Irem Boybat and has published in prestigious journals such as Nature, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Hsinyu Tsai

84 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Equivalent-accuracy accelerated neural-network training using analogue memory

2018 770 citations Stefano Ambrogio, Pritish Narayanan et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hsinyu Tsai United States	26	2.2k	1.0k	446	333	299	89	2.8k
Hussein Nili United States	24	2.3k 1.1×	1.8k 1.8×	212 0.5×	565 1.7×	449 1.5×	45	3.8k
Carlo Ricciardi Italy	29	2.3k 1.1×	650 0.6×	399 0.9×	637 1.9×	763 2.6×	138	3.2k
Zengguang Cheng China	19	2.1k 0.9×	1.5k 1.5×	678 1.5×	624 1.9×	1.1k 3.8×	40	3.3k
Dong Wu China	19	1.3k 0.6×	265 0.3×	171 0.4×	262 0.8×	838 2.8×	78	2.4k
Swapnadeep Poddar Hong Kong	24	2.1k 0.9×	1.1k 1.1×	93 0.2×	252 0.8×	487 1.6×	41	2.4k
Changhwan Choi South Korea	38	3.8k 1.8×	1.2k 1.2×	320 0.7×	1.2k 3.6×	324 1.1×	154	4.2k
Gil Ju Lee South Korea	27	1.5k 0.7×	622 0.6×	137 0.3×	531 1.6×	1.1k 3.7×	92	3.2k
You Yin Japan	20	1.2k 0.6×	803 0.8×	140 0.3×	280 0.8×	252 0.8×	118	1.5k
Feichi Zhou China	22	3.3k 1.5×	1.1k 1.1×	612 1.4×	1.1k 3.4×	474 1.6×	55	4.0k
Shuiyuan Wang China	19	2.3k 1.1×	1.5k 1.5×	385 0.9×	592 1.8×	394 1.3×	32	3.0k

Countries citing papers authored by Hsinyu Tsai

Since Specialization

Citations

This map shows the geographic impact of Hsinyu Tsai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hsinyu Tsai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hsinyu Tsai more than expected).

Fields of papers citing papers by Hsinyu Tsai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hsinyu Tsai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hsinyu Tsai. The network helps show where Hsinyu Tsai may publish in the future.

Co-authorship network of co-authors of Hsinyu Tsai

This figure shows the co-authorship network connecting the top 25 collaborators of Hsinyu Tsai. A scholar is included among the top collaborators of Hsinyu Tsai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hsinyu Tsai. Hsinyu Tsai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ambrogio, Stefano, Pritish Narayanan, Charles Mackin, et al.. (2025). Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip. Nature Communications. 16(1). 8661–8661. 1 indexed citations

Tsai, Hsinyu, et al.. (2025). Progress and challenges of phase change memory for in-memory computing. Current Opinion in Solid State and Materials Science. 37. 101225–101225.

Simon, William, Irem Boybat, Gagandeep Singh, et al.. (2025). CiMBA: Accelerating Genome Sequencing Through On-Device Basecalling via Compute-in-Memory. IEEE Transactions on Parallel and Distributed Systems. 36(6). 1130–1145. 1 indexed citations

Büchel, Julian, William Simon, Irem Boybat, et al.. (2025). Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing. Nature Computational Science. 5(1). 13–26. 3 indexed citations

Lin, Liang-Yi, et al.. (2025). Redox-induced engineering of amorphous/crystalline MnFeOx catalyst enables H2O/SO2-tolerant NOx abatement at ultra-low temperatures. Journal of Hazardous Materials. 489. 137618–137618. 4 indexed citations

Lee, Huan‐Fang, et al.. (2025). Preventing ICU ‐Acquired Weakness With Early Rehabilitation: An Umbrella Review of Systematic Reviews and Meta‐Analysis. Nursing in Critical Care. 30(4). e70113–e70113.

Shie, Kai-Cheng, et al.. (2024). Chemical mechanical planarization of nanotwinned copper/polyimide for low temperature hybrid bonding. Journal of Electroanalytical Chemistry. 969. 118544–118544. 6 indexed citations

Chen, An, Stefano Ambrogio, Pritish Narayanan, et al.. (2024). (Invited) Emerging Nonvolatile Memories for Analog Neuromorphic Computing. ECS Meeting Abstracts. MA2024-01(21). 1293–1293. 1 indexed citations

Li, Ning, Hsinyu Tsai, Vijay Narayanan, & Malte J. Rasch. (2023). Impact of analog memory device failure on in-memory computing inference accuracy. SHILAP Revista de lepidopterología. 1(1). 3 indexed citations

10.

Gallo, Manuel Le, Corey Lammie, Julian Büchel, et al.. (2023). Using the IBM analog in-memory hardware acceleration kit for neural network training and inference. SHILAP Revista de lepidopterología. 1(4). 27 indexed citations

11.

Rasch, Malte J., Charles Mackin, Manuel Le Gallo, et al.. (2023). Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. Nature Communications. 14(1). 5282–5282. 77 indexed citations

12.

Jain, Shubham, Hsinyu Tsai, R. Muralidhar, et al.. (2022). A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 31(1). 114–127. 28 indexed citations

13.

Mackin, Charles, Malte J. Rasch, An Chen, et al.. (2022). Optimised weight programming for analogue memory-based deep neural networks. Nature Communications. 13(1). 3765–3765. 34 indexed citations

14.

Wu, Yudan, et al.. (2022). Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke. Medical & Biological Engineering & Computing. 60(10). 2841–2849. 10 indexed citations

15.

Tsai, Hsinyu, An Chen, Malte J. Rasch, et al.. (2021). Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. Frontiers in Computational Neuroscience. 15. 675741–675741. 16 indexed citations

16.

Mackin, Charles, Pritish Narayanan, Stefano Ambrogio, et al.. (2020). Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges. 1–10. 4 indexed citations

17.

Mackin, Charles, et al.. (2019). Accelerating Deep Neural Networks with Analog Memory Devices. Bulletin of the American Physical Society. 2019. 1 indexed citations

18.

Kim, Seyoung, Xiaoyu Sun, P. M. Solomon, et al.. (2018). Capacitor-based Cross-point Array for Analog Neural Network with Record Symmetry and Linearity. 25–26. 33 indexed citations

19.

Chang, Tsung-Yao, Min Huang, Ahmet Ali Yanik, et al.. (2011). Large-scale plasmonic microarrays for label-free high-throughput screening. Lab on a Chip. 11(21). 3596–3596. 87 indexed citations

20.

Tsai, Inn‐Ho, Hsinyu Tsai, Yingming Wang, Tun-Pe, & David A. Warrell. (2007). Venom phospholipases of Russell's vipers from Myanmar and eastern India—Cloning, characterization and phylogeographic analysis. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1774(8). 1020–1028. 31 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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