Ho-Cheung Ng

497 total citations
23 papers, 284 citations indexed

About

Ho-Cheung Ng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Hardware and Architecture. According to data from OpenAlex, Ho-Cheung Ng has authored 23 papers receiving a total of 284 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Hardware and Architecture. Recurrent topics in Ho-Cheung Ng's work include Advanced Neural Network Applications (6 papers), Embedded Systems Design Techniques (4 papers) and Parallel Computing and Optimization Techniques (4 papers). Ho-Cheung Ng is often cited by papers focused on Advanced Neural Network Applications (6 papers), Embedded Systems Design Techniques (4 papers) and Parallel Computing and Optimization Techniques (4 papers). Ho-Cheung Ng collaborates with scholars based in United Kingdom, Hong Kong and China. Ho-Cheung Ng's co-authors include Wayne Luk, Shuanglong Liu, Xinyu Niu, Hayden Kwok‐Hay So, Zhiqiang Que, Hongxiang Fan, George A. Constantinides, Erwei Wang, Rongxuan Zhao and Cheng Liu and has published in prestigious journals such as Science Advances, ACM Computing Surveys and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Ho-Cheung Ng

22 papers receiving 281 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ho-Cheung Ng United Kingdom	10	138	115	83	62	53	23	284
Bingyi Zhang United States	10	153 1.1×	102 0.9×	159 1.9×	31 0.5×	34 0.6×	31	287
Yuhong Li China	7	203 1.5×	63 0.5×	86 1.0×	31 0.5×	22 0.4×	14	293
Ranjie Duan China	5	155 1.1×	47 0.4×	312 3.8×	50 0.8×	14 0.3×	7	405
Ali Azarpeyvand Iran	8	51 0.4×	128 1.1×	51 0.6×	58 0.9×	43 0.8×	38	239
Kristof Denolf United States	11	123 0.9×	108 0.9×	34 0.4×	123 2.0×	94 1.8×	27	334
Stefan Mach Switzerland	10	56 0.4×	218 1.9×	44 0.5×	160 2.6×	56 1.1×	14	359
Georges Quénot France	13	256 1.9×	55 0.5×	138 1.7×	46 0.7×	28 0.5×	54	421
Hung-Sheng Chang Taiwan	9	80 0.6×	223 1.9×	70 0.8×	91 1.5×	88 1.7×	19	322
Mustafa Ali United States	11	58 0.4×	417 3.6×	90 1.1×	103 1.7×	46 0.9×	17	473

Countries citing papers authored by Ho-Cheung Ng

Since Specialization

Citations

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

Fields of papers citing papers by Ho-Cheung Ng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ho-Cheung Ng. 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 Ho-Cheung Ng. The network helps show where Ho-Cheung Ng may publish in the future.

Co-authorship network of co-authors of Ho-Cheung Ng

This figure shows the co-authorship network connecting the top 25 collaborators of Ho-Cheung Ng. A scholar is included among the top collaborators of Ho-Cheung Ng 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 Ho-Cheung Ng. Ho-Cheung Ng 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

Xie, Ruoxiao, Ross Burdis, Kai Xie, et al.. (2025). Permanent magnetic droplet–derived microrobots. Science Advances. 11(28). eadw3172–eadw3172. 3 indexed citations

Liu, Shuanglong, et al.. (2023). Improving Particle Filters with Adaptive Bayesian Resampling for Real-Time Filtering. 521–526. 1 indexed citations

Liu, Junyi, et al.. (2023). Honeycomb: Ordered Key-Value Store Acceleration on an FPGA-Based SmartNIC. IEEE Transactions on Computers. 73(3). 857–871. 4 indexed citations

Wang, Maolin, Kelvin C. M. Lee, Ho-Cheung Ng, et al.. (2021). Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network. IEEE Transactions on Neural Networks and Learning Systems. 33(7). 2853–2866. 17 indexed citations

Ng, Ho-Cheung, et al.. (2021). Reconfigurable Acceleration of Short Read Mapping with Biological Consideration. 229–239. 2 indexed citations

Ng, Ho-Cheung, et al.. (2020). Acceleration of Short Read Alignment with Runtime Reconfiguration. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 256–262. 8 indexed citations

Wang, Erwei, James J. Davis, Rongxuan Zhao, et al.. (2019). Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going. arXiv (Cornell University). 15 indexed citations

Ng, Ho-Cheung, et al.. (2019). Investigating the Feasibility of FPGA-based Network Switches. Spiral (Imperial College London). 3. 218–226. 5 indexed citations

Wang, Erwei, James J. Davis, Rongxuan Zhao, et al.. (2019). Deep Neural Network Approximation for Custom Hardware. ACM Computing Surveys. 52(2). 1–39. 50 indexed citations

10.

Fan, Hongxiang, Ho-Cheung Ng, Shuanglong Liu, et al.. (2018). Reconfigurable Acceleration of 3D-CNNs for Human Action Recognition with Block Floating-Point Representation. 287–2877. 19 indexed citations

11.

Zhao, Rongxuan, Shuanglong Liu, Ho-Cheung Ng, et al.. (2018). Hardware Compilation of Deep Neural Networks: An Overview. Spiral (Imperial College London). 1–8. 8 indexed citations

12.

Ng, Ho-Cheung, Shuanglong Liu, & Wayne Luk. (2018). ADAM. 189–198. 9 indexed citations

13.

Fan, Hongxiang, Shuanglong Liu, Martin Ferianc, et al.. (2018). A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA. 14–21. 57 indexed citations

14.

Ng, Ho-Cheung, et al.. (2017). A Parameterizable Activation Function Generator for FPGA-Based Neural Network Applications. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 84–84. 2 indexed citations

15.

Ng, Ho-Cheung, Shuanglong Liu, & Wayne Luk. (2017). Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts. Spiral (Imperial College London). 1–8. 20 indexed citations

16.

Wang, Maolin, et al.. (2016). Towards FPGA-assisted spark: An SVM training acceleration case study. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–6. 2 indexed citations

17.

Wang, Maolin, Ho-Cheung Ng, Manish Kumar Jaiswal, et al.. (2016). Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 261–264. 2 indexed citations

18.

Ng, Ho-Cheung, et al.. (2016). High-throughput microparticle screening by 1-μm time-stretch optofluidic imaging integrated with a field-programmable gate array platform. Conference on Lasers and Electro-Optics. STh3G.4–STh3G.4. 3 indexed citations

19.

Ng, Ho-Cheung, Maolin Wang, Manish Kumar Jaiswal, et al.. (2016). High-throughput cellular imaging with high-speed asymmetric-detection time-stretch optical microscopy under FPGA platform. The HKU Scholars Hub (University of Hong Kong). 1–6. 2 indexed citations

20.

Liu, Cheng, Ho-Cheung Ng, & Hayden Kwok‐Hay So. (2015). QuickDough: A rapid FPGA loop accelerator design framework using soft CGRA overlay. 56–63. 26 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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