Zongwu Wang

496 total citations
43 papers, 316 citations indexed

About

Zongwu Wang is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Zongwu Wang has authored 43 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Electrical and Electronic Engineering, 12 papers in Artificial Intelligence and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Zongwu Wang's work include Advanced Memory and Neural Computing (22 papers), Ferroelectric and Negative Capacitance Devices (18 papers) and Neural dynamics and brain function (8 papers). Zongwu Wang is often cited by papers focused on Advanced Memory and Neural Computing (22 papers), Ferroelectric and Negative Capacitance Devices (18 papers) and Neural dynamics and brain function (8 papers). Zongwu Wang collaborates with scholars based in China, United States and United Kingdom. Zongwu Wang's co-authors include Fangxin Liu, Li Jiang, Wenbo Zhao, Zhezhi He, Xiaoyao Liang, Yilong Zhao, Haoming Li, Zhuoran Song, Yanzhi Wang and Ning Yang and has published in prestigious journals such as IEEE Transactions on Computers, Frontiers in Neuroscience and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Zongwu Wang

35 papers receiving 313 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zongwu Wang China 10 206 121 73 72 41 43 316
Daisuke Miyashita Japan 12 400 1.9× 132 1.1× 112 1.5× 21 0.3× 33 0.8× 35 501
Yun Long United States 10 329 1.6× 111 0.9× 108 1.5× 25 0.3× 41 1.0× 27 407
Yi Kang China 9 221 1.1× 75 0.6× 41 0.6× 26 0.4× 55 1.3× 65 308
Yuxiang Fu China 11 243 1.2× 92 0.8× 45 0.6× 29 0.4× 55 1.3× 69 377
Dingheng Wang China 9 136 0.7× 116 1.0× 88 1.2× 91 1.3× 22 0.5× 15 304
Deepak Kadetotad United States 11 292 1.4× 103 0.9× 62 0.8× 70 1.0× 44 1.1× 26 442
Daniel Bankman United States 10 361 1.8× 121 1.0× 102 1.4× 15 0.2× 33 0.8× 13 437
Zheng Qu United States 9 114 0.6× 137 1.1× 120 1.6× 20 0.3× 65 1.6× 17 294
Atif Hashmi United States 10 177 0.9× 74 0.6× 45 0.6× 129 1.8× 79 1.9× 17 336
Wenping Zhu China 10 117 0.6× 137 1.1× 122 1.7× 16 0.2× 42 1.0× 34 306

Countries citing papers authored by Zongwu Wang

Since Specialization
Citations

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

Fields of papers citing papers by Zongwu Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zongwu Wang

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

All Works

20 of 20 papers shown
1.
Li, Haoming, Fangxin Liu, Zongwu Wang, et al.. (2025). Attack and Defense: Enhancing Robustness of Binary Hyper-Dimensional Computing. ACM Transactions on Architecture and Code Optimization. 22(3). 1–25.
2.
Liu, Fangxin, Haoming Li, Zongwu Wang, et al.. (2025). ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering. 18–33.
3.
4.
Li, Haoming, et al.. (2025). NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks. 734–740. 4 indexed citations
5.
6.
Liu, Fangxin, Zongwu Wang, Wenbo Zhao, et al.. (2024). Exploiting Temporal-Unrolled Parallelism for Energy-Efficient SNN Acceleration. IEEE Transactions on Parallel and Distributed Systems. 35(10). 1749–1764. 5 indexed citations
7.
Liu, Fangxin, et al.. (2024). LowPASS: A Low power PIM-based accelerator with Speculative Scheme for SNNs. 1–6. 1 indexed citations
8.
Yang, Ning, Fangxin Liu, Zongwu Wang, Jijun Zhao, & Li Jiang. (2024). SearchQ: Search-Based Fine-Grained Quantization for Data-Free Model Compression. 1(2). 220–228.
9.
Liu, Fangxin, et al.. (2024). SpMMPlu-Pro: An Enhanced Compiler Plug-In for Efficient SpMM and Sparsity Propagation Algorithm. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(2). 669–683. 1 indexed citations
10.
Liu, Fangxin, Zongwu Wang, Haoming Li, et al.. (2024). INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding. 1–6. 2 indexed citations
11.
Zhao, Yilong, Mingyu Gao, Fangxin Liu, et al.. (2024). UM-PIM: DRAM-based PIM with Uniform & Shared Memory Space. 644–659. 4 indexed citations
12.
Wang, Zongwu, et al.. (2024). PS4: A Low Power SNN Accelerator with Spike Speculative Scheme. 76–83. 1 indexed citations
13.
14.
Liu, Fangxin, et al.. (2023). ERA-BS: Boosting the Efficiency of ReRAM-Based PIM Accelerator With Fine-Grained Bit-Level Sparsity. IEEE Transactions on Computers. 73(9). 2320–2334. 9 indexed citations
15.
16.
Liu, Fangxin, Wenbo Zhao, Zongwu Wang, et al.. (2022). IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41(12). 5313–5326. 12 indexed citations
17.
Liu, Fangxin, et al.. (2022). SoBS-X: Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42(1). 204–217. 8 indexed citations
18.
Wang, Zongwu, Zhezhi He, Rui Yang, et al.. (2022). Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). 1251–1256. 1 indexed citations
19.
Liu, Fangxin, Wenbo Zhao, Zhezhi He, et al.. (2021). SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. 417–424. 16 indexed citations
20.
Liu, Fangxin, et al.. (2021). SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training. Frontiers in Neuroscience. 15. 756876–756876. 39 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Daisuke Miyashita Breakdown of academic impact, for papers by Yun Long Breakdown of academic impact, for papers by Yi Kang Breakdown of academic impact, for papers by Yuxiang Fu Breakdown of academic impact, for papers by Dingheng Wang Breakdown of academic impact, for papers by Deepak Kadetotad Breakdown of academic impact, for papers by Daniel Bankman Breakdown of academic impact, for papers by Zheng Qu Breakdown of academic impact, for papers by Atif Hashmi Breakdown of academic impact, for papers by Wenping Zhu
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