Zidong Du

5.4k total citations · 3 hit papers
68 papers, 3.6k citations indexed

About

Zidong Du is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Zidong Du has authored 68 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 38 papers in Electrical and Electronic Engineering and 29 papers in Artificial Intelligence. Recurrent topics in Zidong Du's work include Advanced Neural Network Applications (31 papers), Advanced Memory and Neural Computing (26 papers) and Parallel Computing and Optimization Techniques (23 papers). Zidong Du is often cited by papers focused on Advanced Neural Network Applications (31 papers), Advanced Memory and Neural Computing (26 papers) and Parallel Computing and Optimization Techniques (23 papers). Zidong Du collaborates with scholars based in China, United States and France. Zidong Du's co-authors include Yunji Chen, Tianshi Chen, Olivier Temam, Chengyong Wu, Ninghui Sun, Jia Wang, Tao Luo, Ling Li, Shaoli Liu and Paolo Ienne and has published in prestigious journals such as Proceedings of the IEEE, IEEE Transactions on Image Processing and Neurocomputing.

In The Last Decade

Zidong Du

58 papers receiving 3.5k 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.

DianNao

2014 944 citations Tianshi Chen, Zidong Du et al. profile →
ShiDianNao

2015 657 citations Zidong Du, Tianshi Chen et al. profile →
Cambricon-X: An accelerator for sparse neural networks

2016 432 citations Shijin Zhang, Zidong Du et al. profile →

Peers

Zidong Du

EEE

CVPR

AI

HA

CNC

Yunji Chen China

Shaojun Wei China

Shouyi Yin China

Rangharajan Venkatesan United States

Leibo Liu China

Huizi Mao United States

Xuehai Qian United States

Brucek Khailany United States

Jing Pu United States

Angshuman Parashar United States

Yunji Chen China

Zidong Du

3.4k ×1.6

EEE

3.0k ×1.4

CVPR

1.8k ×1.5

AI

1.8k ×2.0

HA

1.1k ×2.3

CNC

Citations per year, relative to Zidong Du Zidong Du (= 1×) peers Yunji Chen

Countries citing papers authored by Zidong Du

Since Specialization

Citations

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

Fields of papers citing papers by Zidong Du

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zidong Du

This figure shows the co-authorship network connecting the top 25 collaborators of Zidong Du. A scholar is included among the top collaborators of Zidong Du 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 Zidong Du. Zidong Du 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

1.

Ruan, Tingting, Rui Zhang, Di Huang, et al.. (2025). Mosaic: Exploiting Instruction-Level Parallelism on Deep Learning Accelerators with iTex Tessellation. 672–688.

2.

Zhao, Yang, Di Huang, Qi Lei, et al.. (2025). CodeV: Empowering LLMs With HDL Generation Through Multilevel Summarization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 45(4). 1893–1906. 3 indexed citations

3.

Sun, Yifan, Zhouping Yin, Xing Hu, et al.. (2025). TensorFHE+: Fully Homomorphic Encryption Acceleration Based on Linear Algebra. IEEE Transactions on Computers. 75(2). 612–627.

4.

Zhang, Zhenxing, Yuanbo Wen, Hairong Lyu, et al.. (2025). AI Computing Systems for Large Language Models Training. Journal of Computer Science and Technology. 40(1). 6–41. 1 indexed citations

5.

Kong, Weihao, Qi Guo, Yongwei Zhao, et al.. (2024). Cambricon-D: Full-Network Differential Acceleration for Diffusion Models. 903–914. 5 indexed citations

6.

Zhang, Rui, Yuxuan Guo, Zidong Du, et al.. (2024). Emergent Communication for Numerical Concepts Generalization. Proceedings of the AAAI Conference on Artificial Intelligence. 38(16). 17609–17617.

7.

Chen, Yi, Yongwei Zhao, Yanwei Dai, et al.. (2024). Cambricon-C: Efficient 4-Bit Matrix Unit via Primitivization. 538–550.

8.

Yu, Zhongkai, Jie Zhang, Tian Zhi, et al.. (2024). Cambricon-LLM: A Chiplet-Based Hybrid Architecture for On-Device Inference of 70B LLM. 1474–1488. 4 indexed citations

9.

Li, Xiaqing, Yongwei Zhao, Xing Hu, et al.. (2024). Efficient and Fast High-Performance Library Generation for Deep Learning Accelerators. IEEE Transactions on Computers. 74(1). 155–169.

10.

Duan, Jinhao, Kaidi Xu, Rui Zhang, et al.. (2024). Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?. 24398–24407. 1 indexed citations

11.

Du, Zidong, Yang Liu, Yongwei Zhao, et al.. (2024). Cambricon-M: A Fibonacci-Coded Charge-Domain SRAM-Based CIM Accelerator for DNN Inference. 1663–1677. 1 indexed citations

12.

Huang, Di, et al.. (2023). Online Symbolic Regression with Informative Query. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 5122–5130. 1 indexed citations

13.

Yi, Qi, Rui Zhang, Jiaming Guo, et al.. (2023). Learning controllable elements oriented representations for reinforcement learning. Neurocomputing. 549. 126455–126455. 2 indexed citations

14.

Zhao, Yongwei, Zidong Du, Qi Guo, Zhiwei Xu, & Yunji Chen. (2023). Rescue to the Curse of universality. Science China Information Sciences. 66(9).

15.

Hu, Xing, Rui Zhang, Jiaming Guo, et al.. (2023). Conceptual Reinforcement Learning for Language-Conditioned Tasks. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9426–9434. 1 indexed citations

16.

Chen, Xiaobing, Xinfeng Xie, Xing Hu, et al.. (2021). Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41(4). 936–949. 34 indexed citations

17.

Chen, Yunji, Zidong Du, Shijin Zhang, et al.. (2018). Addressing Sparsity in Deep Neural Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 38(10). 1858–1871. 11 indexed citations

18.

Liu, Shaoli, Zidong Du, Jinhua Tao, et al.. (2016). Cambricon: An Instruction Set Architecture for Neural Networks. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 393–405. 76 indexed citations

19.

Du, Zidong, Ying Wang, Huawei Li, et al.. (2015). Retraining-based timing error mitigation for hardware neural networks. Design, Automation, and Test in Europe. 593–596. 13 indexed citations

20.

Chen, Tianshi, Zidong Du, Ninghui Sun, et al.. (2014). DianNao. ACM SIGARCH Computer Architecture News. 42(1). 269–284. 213 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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