Huiying Lan

680 total citations · 1 hit paper
8 papers, 476 citations indexed

About

Huiying Lan is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Hardware and Architecture. According to data from OpenAlex, Huiying Lan has authored 8 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Electrical and Electronic Engineering and 3 papers in Hardware and Architecture. Recurrent topics in Huiying Lan's work include Advanced Neural Network Applications (5 papers), Advanced Memory and Neural Computing (5 papers) and Parallel Computing and Optimization Techniques (3 papers). Huiying Lan is often cited by papers focused on Advanced Neural Network Applications (5 papers), Advanced Memory and Neural Computing (5 papers) and Parallel Computing and Optimization Techniques (3 papers). Huiying Lan collaborates with scholars based in China, Switzerland and Singapore. Huiying Lan's co-authors include Zidong Du, Yunji Chen, Tianshi Chen, Shaoli Liu, Shijin Zhang, Lei Zhang, Ling Li, Qi Guo, Qi Guo and Ling Li and has published in prestigious journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ACM Transactions on Computer Systems and Journal of Computer Science and Technology.

In The Last Decade

Huiying Lan

7 papers receiving 466 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.

Cambricon-X: An accelerator for sparse neural networks

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

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Huiying Lan China	4	332	258	202	116	39	8	476
Qingcheng Xiao China	9	389 1.2×	306 1.2×	149 0.7×	150 1.3×	50 1.3×	11	567
Manoj Alwani India	3	345 1.0×	253 1.0×	139 0.7×	93 0.8×	42 1.1×	7	465
Shibin Tang China	7	361 1.1×	316 1.2×	160 0.8×	79 0.7×	42 1.1×	7	560
Kevin Siu Canada	8	251 0.8×	191 0.7×	142 0.7×	67 0.6×	31 0.8×	10	356
Jiecao Yu United States	6	250 0.8×	163 0.6×	183 0.9×	88 0.8×	52 1.3×	7	365
Guohao Dai China	10	160 0.5×	199 0.8×	157 0.8×	113 1.0×	84 2.2×	46	432
David J. Palframan United States	6	223 0.7×	188 0.7×	169 0.8×	133 1.1×	83 2.1×	10	380
Philipp Gysel United States	4	251 0.8×	197 0.8×	123 0.6×	47 0.4×	25 0.6×	5	347
Matthieu Courbariaux Canada	3	311 0.9×	167 0.6×	219 1.1×	38 0.3×	19 0.5×	4	436
Dipankar Das India	8	347 1.0×	171 0.7×	221 1.1×	219 1.9×	176 4.5×	16	617

Countries citing papers authored by Huiying Lan

Since Specialization

Citations

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

Fields of papers citing papers by Huiying Lan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Huiying Lan

This figure shows the co-authorship network connecting the top 25 collaborators of Huiying Lan. A scholar is included among the top collaborators of Huiying Lan 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 Huiying Lan. Huiying Lan 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:

8 of 8 papers shown

1.

Song, Yijun, Xia Li, Yifei Sun, et al.. (2025). Efficient Partitioning Vision Transformer on Edge Devices for Distributed Inference. 286–296.

2.

Lan, Huiying, et al.. (2019). Assembly language and assembler for deep learning accelerators. 25(4). 386–394. 2 indexed citations

3.

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

4.

Chen, Yunji, Huiying Lan, Zidong Du, et al.. (2018). An Instruction Set Architecture for Machine Learning. ACM Transactions on Computer Systems. 36(3). 1–35. 10 indexed citations

5.

Tao, Jinhua, Zidong Du, Qi Guo, et al.. (2018). BenchIP: Benchmarking Intelligence Processors. Journal of Computer Science and Technology. 33(1). 1–23. 15 indexed citations

6.

Lan, Huiying, Xiao Zhang, Jinhua Tao, et al.. (2017). DLPlib: A Library for Deep Learning Processor. Journal of Computer Science and Technology. 32(2). 286–296. 3 indexed citations

7.

Zhang, Shijin, Zidong Du, Lei Zhang, et al.. (2016). Cambricon-X: An accelerator for sparse neural networks. 1–12. 432 indexed citations breakdown →

8.

Liu, Jinshuo, Huiying Lan, Yizhen Fu, Hui Wu, & Peng Li. (2012). Analyzing electricity consumption via data mining. Wuhan University Journal of Natural Sciences. 17(2). 121–125. 3 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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