Liangzhen Lai

4.0k total citations · 2 hit papers
31 papers, 977 citations indexed

About

Liangzhen Lai is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Liangzhen Lai has authored 31 papers receiving a total of 977 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Electrical and Electronic Engineering, 13 papers in Hardware and Architecture and 6 papers in Artificial Intelligence. Recurrent topics in Liangzhen Lai's work include Semiconductor materials and devices (10 papers), Low-power high-performance VLSI design (8 papers) and Parallel Computing and Optimization Techniques (8 papers). Liangzhen Lai is often cited by papers focused on Semiconductor materials and devices (10 papers), Low-power high-performance VLSI design (8 papers) and Parallel Computing and Optimization Techniques (8 papers). Liangzhen Lai collaborates with scholars based in United States, Belgium and South Korea. Liangzhen Lai's co-authors include Vikas Chandra, Naveen Suda, Puneet Gupta, Hardik Sharma, Joon Kyung Kim, Jongse Park, Hadi Esmaeilzadeh, Hyoukjun Kwon, David Z. Pan and Meng Li and has published in prestigious journals such as IEEE Transactions on Electron Devices, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems and Solid-State Electronics.

In The Last Decade

Liangzhen Lai

29 papers receiving 955 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.

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

2018 344 citations Hardik Sharma, Jongse Park et al. profile →
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

2022 167 citations Jiaqi Gu, Hyoukjun Kwon et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers

Liangzhen Lai

EEE

CVPR

AI

HA

CNC

Donghyeon Han South Korea

Sanghoon Kang South Korea

Fengbin Tu China

Lingzhi Sui China

Juhyoung Lee South Korea

Tong Geng United States

Liqiang He China

Eunhyeok Park South Korea

Jungwook Choi South Korea

Sungpill Choi South Korea

Donghyeon Han South Korea

Liangzhen Lai

564 ×1.1

EEE

464 ×1.1

CVPR

246 ×0.8

AI

132 ×0.8

HA

62 ×0.7

CNC

Citations per year, relative to Liangzhen Lai Liangzhen Lai (= 1×) peers Donghyeon Han

Countries citing papers authored by Liangzhen Lai

Since Specialization

Citations

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

Fields of papers citing papers by Liangzhen Lai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liangzhen Lai

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

Wasti, Bram, Liangzhen Lai, Anas Mahmoud, et al.. (2024). LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. 12622–12642. 7 indexed citations

2.

Lam, Maximilian, Jeff Johnson, Wenjie Xiong, et al.. (2024). GPU-based Private Information Retrieval for On-Device Machine Learning Inference. VTechWorks (Virginia Tech). 197–214. 4 indexed citations

3.

Kwon, Hyoukjun, et al.. (2023). DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads. 73–86. 7 indexed citations

4.

Zhang, Tengyu, Meng Li, Renjie Wei, et al.. (2023). Efficient Non-Linear Adder for Stochastic Computing with Approximate Spatial-Temporal Sorting Network. 1–6. 1 indexed citations

5.

Gu, Jiaqi, Hyoukjun Kwon, Dilin Wang, et al.. (2022). Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12084–12093. 167 indexed citations breakdown →

6.

Yang, Lei, Zheyu Yan, Hyoukjun Kwon, et al.. (2020). Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks. 1–6. 63 indexed citations

7.

Lai, Liangzhen, et al.. (2019). SenseHAR. 15–28. 36 indexed citations

8.

Sharma, Hardik, Jongse Park, Naveen Suda, et al.. (2018). Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. 764–775. 344 indexed citations breakdown →

9.

Lai, Liangzhen & Naveen Suda. (2018). Enabling deep learning at the IoT edge. 1–6. 33 indexed citations

10.

Li, Meng, Liangzhen Lai, Naveen Suda, Vikas Chandra, & David Z. Pan. (2017). PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training with A Fine-Grained Privacy Control.. arXiv (Cornell University). 9 indexed citations

11.

Lai, Liangzhen & Puneet Gupta. (2016). Hardware Reliability margining for the dark silicon era. 637–642.

12.

Lai, Liangzhen. (2015). Cross-Layer Approaches for Monitoring, Margining and Mitigation of Circuit Variability. eScholarship (California Digital Library). 1 indexed citations

13.

Lai, Liangzhen & Puneet Gupta. (2014). Accurate and inexpensive performance monitoring for variability-aware systems. 467–473. 4 indexed citations

14.

Lai, Liangzhen, Vikas Chandra, Robert Aitken, & Puneet Gupta. (2014). SlackProbe: A Flexible and Efficient In Situ Timing Slack Monitoring Methodology. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 33(8). 1168–1179. 22 indexed citations

15.

Lai, Liangzhen, Vikas Chandra, Robert Aitken, & Puneet Gupta. (2014). BTI-Gater: An Aging-Resilient Clock Gating Methodology. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 4(2). 180–189. 4 indexed citations

16.

Gupta, Puneet, et al.. (2013). Synthesis and Analysis of Design-Dependent Ring Oscillator (DDRO) Performance Monitors. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 22(10). 2117–2130. 20 indexed citations

17.

Wanner, Lucas, et al.. (2013). VarEMU: An emulation testbed for variability-aware software. 1–10. 17 indexed citations

18.

Wang, Wei, et al.. (2011). Thermal modeling of three-dimensional integrated circuits considering the thermal removal capability of different TSVs. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–7. 1 indexed citations

19.

Lai, Liangzhen, et al.. (2010). Analysis of the heat removal capability of the power distribution network in 3D ICs. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–4. 2 indexed citations

20.

Chen, Haoyu, et al.. (2006). NiSi Schottky Barrier Process-Strained Si (SB-PSS) CMOS Technology for High Performance Applications. 80–81. 16 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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