Hanbo Sun

524 total citations
22 papers, 338 citations indexed

About

Hanbo Sun is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Hanbo Sun has authored 22 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 10 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Hanbo Sun's work include Advanced Memory and Neural Computing (15 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Advanced Neural Network Applications (9 papers). Hanbo Sun is often cited by papers focused on Advanced Memory and Neural Computing (15 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Advanced Neural Network Applications (9 papers). Hanbo Sun collaborates with scholars based in China, United States and North Korea. Hanbo Sun's co-authors include Huazhong Yang, Zhenhua Zhu, Yu Wang, Lixue Xia, Guohao Dai, Xiaohong Chen, Yi Cai, Song Han, Yujun Lin and Xuefei Ning and has published in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems and ACM Transactions on Reconfigurable Technology and Systems.

In The Last Decade

Hanbo Sun

21 papers receiving 334 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hanbo Sun China	9	252	95	88	44	36	22	338
Yun Long United States	10	329 1.3×	111 1.2×	108 1.2×	41 0.9×	29 0.8×	27	407
Zongwu Wang China	10	206 0.8×	121 1.3×	73 0.8×	41 0.9×	27 0.8×	43	316
Daniel Bankman United States	10	361 1.4×	121 1.3×	102 1.2×	33 0.8×	22 0.6×	13	437
Geoffrey Ndu United Kingdom	5	252 1.0×	96 1.0×	61 0.7×	97 2.2×	60 1.7×	7	340
Rickard Ewetz United States	10	366 1.5×	136 1.4×	72 0.8×	89 2.0×	43 1.2×	76	486
Shengyuan Zhou China	8	213 0.8×	122 1.3×	173 2.0×	95 2.2×	51 1.4×	14	378
En-Yu Yang United States	8	494 2.0×	151 1.6×	131 1.5×	91 2.1×	58 1.6×	15	620
Yun-Chen Lo Taiwan	7	311 1.2×	78 0.8×	47 0.5×	37 0.8×	19 0.5×	18	356
Kodai Ueyoshi Japan	9	373 1.5×	115 1.2×	185 2.1×	63 1.4×	37 1.0×	22	455
Tai-Hao Wen Taiwan	8	312 1.2×	74 0.8×	30 0.3×	41 0.9×	21 0.6×	12	343

Countries citing papers authored by Hanbo Sun

Since Specialization

Citations

This map shows the geographic impact of Hanbo Sun'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 Hanbo Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hanbo Sun more than expected).

Fields of papers citing papers by Hanbo Sun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hanbo Sun. 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 Hanbo Sun. The network helps show where Hanbo Sun may publish in the future.

Co-authorship network of co-authors of Hanbo Sun

This figure shows the co-authorship network connecting the top 25 collaborators of Hanbo Sun. A scholar is included among the top collaborators of Hanbo Sun 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 Hanbo Sun. Hanbo Sun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhu, Zhenhua, Hanbo Sun, Xuefei Ning, et al.. (2024). Toward High-Accuracy and Real-Time Two-Stage Small Object Detection on FPGA. IEEE Transactions on Circuits and Systems for Video Technology. 34(9). 8053–8066. 5 indexed citations

Zeng, Shulin, et al.. (2024). FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs. 223–234. 49 indexed citations

Zhu, Yu, Zhenhua Zhu, Guohao Dai, et al.. (2023). PIM-HLS: An Automatic Hardware Generation Tool for Heterogeneous Processing-In-Memory-based Neural Network Accelerators. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–6. 4 indexed citations

Zhu, Zhenhua, Hanbo Sun, Tongxin Xie, et al.. (2023). MNSIM 2.0: A Behavior-Level Modeling Tool for Processing-In-Memory Architectures. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42(11). 4112–4125. 24 indexed citations

Sun, Hanbo, et al.. (2023). Human Transcription Quality Improvement. 3053–3057.

Dong, Bo, et al.. (2022). CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost. 35–43. 6 indexed citations

Liu, Jun, Zhenhua Zhu, Hanbo Sun, et al.. (2022). Optimizing Graph-based Approximate Nearest Neighbor Search: Stronger and Smarter. 179–184. 2 indexed citations

Sun, Hanbo, Chenyu Wang, Zhenhua Zhu, et al.. (2022). Gibbon: Efficient Co-Exploration of NN Model and Processing-In-Memory Architecture. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). 867–872. 8 indexed citations

Sun, Hanbo, Zhenhua Zhu, Yi Cai, et al.. (2021). Reliability-Aware Training and Performance Modeling for Processing-In-Memory Systems. 2018. 847–852. 1 indexed citations

10.

Zeng, Shulin, Guohao Dai, Hanbo Sun, et al.. (2021). A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud. ACM Transactions on Reconfigurable Technology and Systems. 15(3). 1–31. 3 indexed citations

11.

Zhu, Zhenhua, et al.. (2021). MNSIM-TIME: Performance Modeling Framework for Training-In-Memory Architectures. 27. 1–4. 4 indexed citations

12.

Zeng, Shulin, Guohao Dai, Hanbo Sun, et al.. (2021). 3M-AI: A Multi-task and Multi-core Virtualization Framework for Multi-FPGA AI Systems in the Cloud. 228–228. 2 indexed citations

13.

Zeng, Shulin, Hanbo Sun, Xuefei Ning, et al.. (2020). Black Box Search Space Profiling for Accelerator-Aware Neural Architecture Search. 518–523. 6 indexed citations

14.

Sun, Hanbo, Ji Liu, Lu Tian, et al.. (2020). Feature Variance Regularization: A Simple Way to Improve the Generalizability of Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4190–4197. 3 indexed citations

15.

Sun, Hanbo, Zhenhua Zhu, Yi Cai, et al.. (2020). An Energy-Efficient Quantized and Regularized Training Framework For Processing-In-Memory Accelerators. 325–330. 35 indexed citations

16.

Zeng, Shulin, Guohao Dai, Kai Zhong, et al.. (2020). Enable Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud. 317–317. 1 indexed citations

17.

Zhu, Zhenhua, Hanbo Sun, Lixue Xia, et al.. (2020). MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 83–88. 53 indexed citations

18.

Zhu, Zhenhua, Hanbo Sun, Yujun Lin, et al.. (2019). A Configurable Multi-Precision CNN Computing Framework Based on Single Bit RRAM. 1–6. 67 indexed citations

19.

Xia, Lixue, Zhenhua Zhu, Hanbo Sun, et al.. (2018). Rescuing memristor-based computing with non-linear resistance levels. 407–412. 13 indexed citations

20.

Zhu, Zhenhua, Ming Cheng, Lixue Xia, et al.. (2018). Mixed size crossbar based RRAM CNN accelerator with overlapped mapping method. 1–8. 42 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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