Steve Dai

1.0k total citations
27 papers, 677 citations indexed

About

Steve Dai is a scholar working on Hardware and Architecture, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Steve Dai has authored 27 papers receiving a total of 677 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Hardware and Architecture, 13 papers in Computer Networks and Communications and 13 papers in Electrical and Electronic Engineering. Recurrent topics in Steve Dai's work include Embedded Systems Design Techniques (19 papers), Parallel Computing and Optimization Techniques (16 papers) and Interconnection Networks and Systems (13 papers). Steve Dai is often cited by papers focused on Embedded Systems Design Techniques (19 papers), Parallel Computing and Optimization Techniques (16 papers) and Interconnection Networks and Systems (13 papers). Steve Dai collaborates with scholars based in United States, Hong Kong and Cayman Islands. Steve Dai's co-authors include Zhiru Zhang, Gai Liu, Ritchie Zhao, Mingxing Tan, Yuan Zhou, Brucek Khailany, Rangharajan Venkatesan, William J. Dally, Ben Keller and Udit Gupta 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

Steve Dai

27 papers receiving 668 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Steve Dai United States	15	486	319	240	101	96	27	677
Ritchie Zhao United States	12	317 0.7×	297 0.9×	181 0.8×	180 1.8×	263 2.7×	15	660
Brett H. Meyer Canada	17	342 0.7×	468 1.5×	265 1.1×	170 1.7×	59 0.6×	79	756
Chengmo Yang United States	16	545 1.1×	489 1.5×	490 2.0×	148 1.5×	61 0.6×	100	886
Ahmed Sanaullah United States	12	188 0.4×	186 0.6×	105 0.4×	110 1.1×	96 1.0×	28	407
Giovanni Mariani Switzerland	14	378 0.8×	193 0.6×	284 1.2×	146 1.4×	70 0.7×	38	629
Qiaoyan Yu United States	18	539 1.1×	597 1.9×	274 1.1×	217 2.1×	40 0.4×	96	856
Victor A. Ying United States	6	256 0.5×	303 0.9×	160 0.7×	117 1.2×	188 2.0×	9	497
Benjamin Carrión Schäfer United States	18	796 1.6×	730 2.3×	122 0.5×	97 1.0×	27 0.3×	98	1.0k
Bhagirath Narahari United States	13	240 0.5×	198 0.6×	296 1.2×	94 0.9×	46 0.5×	57	510
Florian Zaruba Switzerland	9	399 0.8×	274 0.9×	221 0.9×	113 1.1×	47 0.5×	19	582

Countries citing papers authored by Steve Dai

Since Specialization

Citations

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

Fields of papers citing papers by Steve Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Steve Dai

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

Wu, Nan, et al.. (2024). Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and Prospect. ACM Transactions on Design Automation of Electronic Systems. 29(4). 1–42. 4 indexed citations

Dai, Steve, et al.. (2023). Efficient Transformer Inference with Statically Structured Sparse Attention. 1–6. 4 indexed citations

Keller, Ben, Rangharajan Venkatesan, Steve Dai, et al.. (2023). A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm. IEEE Journal of Solid-State Circuits. 58(4). 1129–1141. 30 indexed citations

Keller, Ben, Rangharajan Venkatesan, Steve Dai, et al.. (2022). A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm. 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits). 16–17. 27 indexed citations

Dai, Steve, et al.. (2021). Clockwork: Resource-Efficient Static Scheduling for Multi-Rate Image Processing Applications on FPGAs. 186–194. 6 indexed citations

Dai, Steve, et al.. (2021). Clockwork: Resource-Efficient Static Scheduling for Multi-Rate Image Processing Applications on FPGAs. 145–146. 2 indexed citations

Khailany, Brucek, Haoxing Ren, Steve Dai, et al.. (2020). Accelerating Chip Design With Machine Learning. IEEE Micro. 40(6). 23–32. 36 indexed citations

Dai, Steve & Zhiru Zhang. (2019). Improving Scalability of Exact Modulo Scheduling with Specialized Conflict-Driven Learning. 1–6. 8 indexed citations

Venkatesan, Rangharajan, Yakun Sophia Shao, Jason Clemons, et al.. (2019). MAGNet: A Modular Accelerator Generator for Neural Networks. 1–8. 84 indexed citations

10.

Dai, Steve, Yuan Zhou, Hang Zhang, et al.. (2018). Fast and Accurate Estimation of Quality of Results in High-Level Synthesis with Machine Learning. 129–132. 77 indexed citations

11.

Zhou, Yuan, Udit Gupta, Steve Dai, et al.. (2018). Rosetta. 269–278. 84 indexed citations

12.

Dai, Steve, et al.. (2018). High-level synthesis with timing-sensitive information flow enforcement. 1–8. 17 indexed citations

13.

Dai, Steve, Gai Liu, & Zhiru Zhang. (2018). A Scalable Approach to Exact Resource-Constrained Scheduling Based on a Joint SDC and SAT Formulation. 137–146. 13 indexed citations

14.

Liu, Gai, Mingxing Tan, Steve Dai, Ritchie Zhao, & Zhiru Zhang. (2017). Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 36(11). 1817–1830. 11 indexed citations

15.

Dai, Steve, Ritchie Zhao, Gai Liu, et al.. (2017). Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis. 189–194. 29 indexed citations

16.

Srivastava, Nitish, Steve Dai, Rajit Manohar, & Zhiru Zhang. (2017). Accelerating Face Detection on Programmable SoC Using C-Based Synthesis. 195–200. 14 indexed citations

17.

Zhao, Ritchie, Mingxing Tan, Steve Dai, & Zhiru Zhang. (2015). Area-efficient pipelining for FPGA-targeted high-level synthesis. 1–6. 15 indexed citations

18.

Tan, Mingxing, Gai Liu, Ritchie Zhao, Steve Dai, & Zhiru Zhang. (2015). ElasticFlow: A complexity-effective approach for pipelining irregular loop nests. 78–85. 32 indexed citations

19.

Tan, Mingxing, Bin Liu, Steve Dai, & Zhiru Zhang. (2014). Multithreaded pipeline synthesis for data-parallel kernels. International Conference on Computer Aided Design. 718–725. 14 indexed citations

20.

Tan, Mingxing, Bin Liu, Steve Dai, & Zhiru Zhang. (2014). Multithreaded pipeline synthesis for data-parallel kernels. 718–725. 17 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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