Hongwu Jiang

1.5k total citations · 1 hit paper
26 papers, 819 citations indexed

About

Hongwu Jiang is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Hongwu Jiang has authored 26 papers receiving a total of 819 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Electrical and Electronic Engineering, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Hongwu Jiang's work include Advanced Memory and Neural Computing (25 papers), Ferroelectric and Negative Capacitance Devices (23 papers) and Advanced Neural Network Applications (7 papers). Hongwu Jiang is often cited by papers focused on Advanced Memory and Neural Computing (25 papers), Ferroelectric and Negative Capacitance Devices (23 papers) and Advanced Neural Network Applications (7 papers). Hongwu Jiang collaborates with scholars based in United States, Belgium and Taiwan. Hongwu Jiang's co-authors include Shimeng Yu, Shanshi Huang, Xiaochen Peng, Anni Lu, Wantong Li, Xiaoyu Sun, Francky Catthoor, Stefan Cosemans, Ruhui Liu and Yen-Chi Chou and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Computers and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Hongwu Jiang

25 papers receiving 808 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.

Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects

2021 224 citations Shimeng Yu, Hongwu Jiang et al. IEEE Circuits and Systems Magazine profile →

Peers

Hongwu Jiang

EEE

CMN

CVPR

Anni Lu United States

Yen-Cheng Chiu Taiwan

Shanshi Huang United States

Je-Min Hung Taiwan

Chorng-Jung Lin Taiwan

Chuan-Jia Jhang Taiwan

Sheng-Po Huang Taiwan

Fu-Chun Chang Taiwan

Tzu-Hsiang Hsu Taiwan

Wenqin Huangfu United States

Anni Lu United States

Hongwu Jiang

667 ×0.9

EEE

152 ×0.8

107 ×0.8

CMN

62 ×0.6

58 ×0.8

CVPR

Citations per year, relative to Hongwu Jiang Hongwu Jiang (= 1×) peers Anni Lu

Countries citing papers authored by Hongwu Jiang

Since Specialization

Citations

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

Fields of papers citing papers by Hongwu Jiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongwu Jiang

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

Jiang, Hongwu, Wantong Li, Shanshi Huang, & Shimeng Yu. (2022). A 40nm Analog-Input ADC-Free Compute-in-Memory RRAM Macro with Pulse-Width Modulation between Sub-arrays. 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits). 266–267. 35 indexed citations

Li, Wantong, Xiaoyu Sun, Shanshi Huang, Hongwu Jiang, & Shimeng Yu. (2022). A 40-nm MLC-RRAM Compute-in-Memory Macro With Sparsity Control, On-Chip Write-Verify, and Temperature-Independent ADC References. IEEE Journal of Solid-State Circuits. 57(9). 2868–2877. 41 indexed citations

Li, Wantong, et al.. (2022). MAC-ECC: In-Situ Error Correction and Its Design Methodology for Reliable NVM-Based Compute-in-Memory Inference Engine. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 12(4). 835–845. 5 indexed citations

Jiang, Hongwu, Shanshi Huang, Wantong Li, & Shimeng Yu. (2022). ENNA: An Efficient Neural Network Accelerator Design Based on ADC-Free Compute-In-Memory Subarrays. IEEE Transactions on Circuits and Systems I Regular Papers. 70(1). 353–363. 15 indexed citations

Li, Wantong, et al.. (2022). A 40nm RRAM Compute-in-Memory Macro with Parallelism-Preserving ECC for Iso-Accuracy Voltage Scaling. 101–104. 6 indexed citations

Huang, Shanshi, Xiaoyu Sun, Xiaochen Peng, Hongwu Jiang, & Shimeng Yu. (2022). Achieving High In Situ Training Accuracy and Energy Efficiency with Analog Non-Volatile Synaptic Devices. ACM Transactions on Design Automation of Electronic Systems. 27(4). 1–19. 2 indexed citations

Huang, Shanshi, Hongwu Jiang, & Shimeng Yu. (2022). Hardware-aware Quantization/Mapping Strategies for Compute-in-Memory Accelerators. ACM Transactions on Design Automation of Electronic Systems. 28(3). 1–23. 4 indexed citations

Huang, Shanshi, Hongwu Jiang, Xiaochen Peng, Wantong Li, & Shimeng Yu. (2021). Secure XOR-CIM Engine: Compute-In-Memory SRAM Architecture With Embedded XOR Encryption. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29(12). 2027–2039. 14 indexed citations

Jiang, Hongwu, Wantong Li, Shanshi Huang, et al.. (2021). Analog-to-Digital Converter Design Exploration for Compute-in-Memory Accelerators. IEEE Design and Test. 39(2). 48–55. 37 indexed citations

10.

Yu, Shimeng, Hongwu Jiang, Shanshi Huang, Xiaochen Peng, & Anni Lu. (2021). Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects. IEEE Circuits and Systems Magazine. 21(3). 31–56. 224 indexed citations breakdown →

11.

Lu, Anni, Xiaochen Peng, Wantong Li, Hongwu Jiang, & Shimeng Yu. (2021). NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark. Frontiers in Artificial Intelligence. 4. 659060–659060. 38 indexed citations

12.

Lu, Anni, Xiaochen Peng, Wantong Li, Hongwu Jiang, & Shimeng Yu. (2021). NeuroSim Validation with 40nm RRAM Compute-in-Memory Macro. 1–4. 13 indexed citations

13.

Li, Wantong, Shanshi Huang, Xiaoyu Sun, Hongwu Jiang, & Shimeng Yu. (2021). Secure-RRAM: A 40nm 16kb Compute-in-Memory Macro with Reconfigurability, Sparsity Control, and Embedded Security. 1–2. 31 indexed citations

14.

Li, Wantong, Xiaoyu Sun, Hongwu Jiang, Shanshi Huang, & Shimeng Yu. (2021). A 40nm RRAM Compute-in-Memory Macro Featuring On-Chip Write-Verify and Offset-Cancelling ADC References. 79–82. 7 indexed citations

15.

Huang, Shanshi, Xiaoyu Sun, Xiaochen Peng, Hongwu Jiang, & Shimeng Yu. (2020). Overcoming Challenges for Achieving High in-situ Training Accuracy with Emerging Memories. 1025–1030. 10 indexed citations

16.

Jiang, Hongwu, Shanshi Huang, Xiaochen Peng, & Shimeng Yu. (2020). MINT: Mixed-Precision RRAM-Based IN-Memory Training Architecture. 1–5. 23 indexed citations

17.

Peng, Xiaochen, Shanshi Huang, Hongwu Jiang, Anni Lu, & Shimeng Yu. (2020). DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Training. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 40(11). 2306–2319. 185 indexed citations

18.

Jiang, Hongwu, Xiaochen Peng, Shanshi Huang, & Shimeng Yu. (2020). CIMAT: A Compute-In-Memory Architecture for On-chip Training Based on Transpose SRAM Arrays. IEEE Transactions on Computers. 1–1. 35 indexed citations

19.

Shim, Wonbo, Hongwu Jiang, Xiaochen Peng, & Shimeng Yu. (2020). Architectural Design of 3D NAND Flash based Compute-in-Memory for Inference Engine. 77–85. 10 indexed citations

20.

Jiang, Hongwu, Xiaochen Peng, Shanshi Huang, & Shimeng Yu. (2019). CIMAT. 490–496. 12 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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