Gary Gréwal

836 total citations
83 papers, 539 citations indexed

About

Gary Gréwal is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Gary Gréwal has authored 83 papers receiving a total of 539 indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Hardware and Architecture, 57 papers in Electrical and Electronic Engineering and 18 papers in Artificial Intelligence. Recurrent topics in Gary Gréwal's work include VLSI and FPGA Design Techniques (56 papers), VLSI and Analog Circuit Testing (39 papers) and Embedded Systems Design Techniques (25 papers). Gary Gréwal is often cited by papers focused on VLSI and FPGA Design Techniques (56 papers), VLSI and Analog Circuit Testing (39 papers) and Embedded Systems Design Techniques (25 papers). Gary Gréwal collaborates with scholars based in Canada, Saudi Arabia and United States. Gary Gréwal's co-authors include Shawki Areibi, Timothy J. Martin, Ming Xu, Anthony Vannelli, Christian Fobel, Abeer Al-Hyari, D.K. Banerji, Deborah Stacey, Rozita Dara and Mark Wineberg and has published in prestigious journals such as Applied Intelligence, Journal of Computational Science and ACM Transactions on Design Automation of Electronic Systems.

In The Last Decade

Gary Gréwal

74 papers receiving 513 citations

Peers

Gary Gréwal
Pierre Bricaud United States
M.C. McFarland United States
Alfonso Sánchez‐Macián Spain
Thaddeus J. Kowalski United States
J.A. Rowson United States
Corporate
A.A. Jerraya France
Koen Danckaert Belgium
Henry Chang United States
Yu-Chin Hsu Taiwan
Pierre Bricaud United States
Gary Gréwal
Citations per year, relative to Gary Gréwal Gary Gréwal (= 1×) peers Pierre Bricaud

Countries citing papers authored by Gary Gréwal

Since Specialization
Citations

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

Fields of papers citing papers by Gary Gréwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gary Gréwal

This figure shows the co-authorship network connecting the top 25 collaborators of Gary Gréwal. A scholar is included among the top collaborators of Gary Gréwal 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 Gary Gréwal. Gary Gréwal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Gréwal, Gary, et al.. (2024). A High-Performance Routing Engine for Large-Scale FPGAs. 53–59. 1 indexed citations
3.
Martin, Timothy J., Christopher O. Barnes, Gary Gréwal, & Shawki Areibi. (2023). FPGA Placement: Dynamic Decision Making Via Machine Learning. 12. 1–6.
4.
Martin, Timothy J., et al.. (2023). A Deep-Learning Data-Driven Approach for Reducing FPGA Routing Runtimes. 2 indexed citations
5.
Martin, Timothy J., et al.. (2023). Integrating Machine-Learning Probes in FPGA CAD: Why and How?. IEEE Design and Test. 40(5). 7–14.
6.
Martin, Timothy J., Christopher O. Barnes, Shawki Areibi, & Gary Gréwal. (2022). An Adaptive Sequential Decision Making Flow for FPGAs using Machine Learning. 12. 34–37. 1 indexed citations
7.
Martin, Timothy J., et al.. (2022). Faster FPGA Routing by Forecasting and Pre-Loading Congestion Information. 15–20. 4 indexed citations
8.
9.
Martin, Timothy J., et al.. (2020). A Deep-Learning Framework for Predicting Congestion During FPGA Placement. 138–144. 11 indexed citations
10.
Al-Hyari, Abeer, et al.. (2020). Machine Learning for Congestion Management and Routability Prediction within FPGA Placement. ACM Transactions on Design Automation of Electronic Systems. 25(5). 1–25. 13 indexed citations
11.
Al-Hyari, Abeer, et al.. (2018). Machine-Learning Based Congestion Estimation for Modern FPGAs. 427–4277. 29 indexed citations
12.
Areibi, Shawki, et al.. (2012). A Dynamic Sampling Framework for Multi-class Imbalanced Data. 113–118. 10 indexed citations
13.
Fobel, Christian, Gary Gréwal, & Deborah Stacey. (2011). GPU-Accelerated Wire-Length Estimation for FPGA Placement. 14–23. 4 indexed citations
14.
Gréwal, Gary, et al.. (2010). CellPilot: Seamless communication within Cell BE and heterogeneous clusters. Journal of Physics Conference Series. 256. 12002–12002.
15.
Areibi, Shawki, et al.. (2009). Meta-Heuristic Based Techniques for FPGA Placement: A Study.. 16. 13–33. 5 indexed citations
16.
Gréwal, Gary, Stelian Coros, D.K. Banerji, & Alec Morton. (2006). Comparing a genetic algorithm penalty function and repair heuristic in the DSP application domain. 31–39. 5 indexed citations
17.
Gréwal, Gary, et al.. (2004). Shrubbery: a new algorithm for quickly growing high-quality Steiner trees. 1. 855–862. 1 indexed citations
18.
Gréwal, Gary, et al.. (2003). An evolutionary approach to behavioural-level synthesis. 264–272 Vol.1. 6 indexed citations
19.
Gréwal, Gary, et al.. (2001). Mapping reference code to irregular DSPs within the retargetable, optimizing compiler COGEN(T). International Symposium on Microarchitecture. 192–202. 5 indexed citations
20.
Gréwal, Gary & Thomas C. Wilson. (2001). AN ENHANCED GENETIC ALGORITHM FOR SOLVING THE HIGH-LEVEL SYNTHESIS PROBLEMS OF SCHEDULING, ALLOCATION, AND BINDING. International Journal of Computational Intelligence and Applications. 1(1). 91–110. 6 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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