Vojtěch Mrázek

1.4k total citations
44 papers, 860 citations indexed

About

Vojtěch Mrázek is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Vojtěch Mrázek has authored 44 papers receiving a total of 860 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Electrical and Electronic Engineering, 21 papers in Artificial Intelligence and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Vojtěch Mrázek's work include Low-power high-performance VLSI design (22 papers), VLSI and FPGA Design Techniques (19 papers) and Evolutionary Algorithms and Applications (15 papers). Vojtěch Mrázek is often cited by papers focused on Low-power high-performance VLSI design (22 papers), VLSI and FPGA Design Techniques (19 papers) and Evolutionary Algorithms and Applications (15 papers). Vojtěch Mrázek collaborates with scholars based in Czechia, Austria and United Arab Emirates. Vojtěch Mrázek's co-authors include Zdeněk Vašíček, Lukáš Sekanina, Radek Hrbáček, Muhammad Shafique, Jie Han, Syed Shakib Sarwar, Kaushik Roy, Muhammad Abdullah Hanif, Mohammad Saeed Ansari and B.F. Cockburn and has published in prestigious journals such as IEEE Access, Neural Computation and Applied Soft Computing.

In The Last Decade

Vojtěch Mrázek

39 papers receiving 835 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vojtěch Mrázek Czechia 13 684 234 207 160 106 44 860
Farhana Sheikh United States 16 616 0.9× 201 0.9× 309 1.5× 185 1.2× 91 0.9× 47 899
Hongyang Jia China 11 668 1.0× 218 0.9× 157 0.8× 99 0.6× 37 0.3× 36 826
Kwen‐Siong Chong Singapore 14 416 0.6× 204 0.9× 254 1.2× 100 0.6× 134 1.3× 90 665
Steven Hsu United States 19 868 1.3× 269 1.1× 537 2.6× 150 0.9× 151 1.4× 71 1.2k
Georgios Zervakis Greece 17 685 1.0× 128 0.5× 243 1.2× 101 0.6× 154 1.5× 49 852
S.M. Fakhraie Iran 13 762 1.1× 181 0.8× 259 1.3× 72 0.5× 234 2.2× 94 985
Hakan Yalcin United States 8 404 0.6× 71 0.3× 312 1.5× 192 1.2× 49 0.5× 10 760
Frank K. Gürkaynak Switzerland 17 579 0.8× 307 1.3× 539 2.6× 252 1.6× 95 0.9× 68 1.0k
Stephen G. Tell United States 16 628 0.9× 155 0.7× 302 1.5× 191 1.2× 67 0.6× 41 934
J.G. Delgado-Frias United States 17 623 0.9× 200 0.9× 332 1.6× 69 0.4× 91 0.9× 143 1.0k

Countries citing papers authored by Vojtěch Mrázek

Since Specialization
Citations

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

Fields of papers citing papers by Vojtěch Mrázek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vojtěch Mrázek

This figure shows the co-authorship network connecting the top 25 collaborators of Vojtěch Mrázek. A scholar is included among the top collaborators of Vojtěch Mrázek 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 Vojtěch Mrázek. Vojtěch Mrázek 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.
Vašíček, Zdeněk, Vojtěch Mrázek, & Lukáš Sekanina. (2024). Automated Verifiability-Driven Design of Approximate Circuits: Exploiting Error Analysis. 1–6.
2.
Sekanina, Lukáš, et al.. (2024). ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers. 1–8. 1 indexed citations
3.
Mrázek, Vojtěch, et al.. (2023). Prediction of Inference Energy on CNN Accelerators Supporting Approximate Circuits. 1 indexed citations
4.
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6.
Marchisio, Alberto, Vojtěch Mrázek, Andrea Massa, et al.. (2022). RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks. IEEE Access. 10. 109043–109055. 7 indexed citations
7.
Marchisio, Alberto, Vojtěch Mrázek, Muhammad Abdullah Hanif, & Muhammad Shafique. (2021). FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29(4). 716–729. 9 indexed citations
8.
Marchisio, Alberto, Beatrice Bussolino, Vojtěch Mrázek, et al.. (2020). A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress. 34–36. 3 indexed citations
9.
Marchisio, Alberto, Vojtěch Mrázek, Muhammad Abdullah Hanif, & Muhammad Shafique. (2020). DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 40(9). 1768–1781. 10 indexed citations
10.
Ansari, Mohammad Saeed, Vojtěch Mrázek, B.F. Cockburn, et al.. (2019). Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28(2). 317–328. 99 indexed citations
11.
Mrázek, Vojtěch, Zdeněk Vašíček, Lukáš Sekanina, Muhammad Abdullah Hanif, & Muhammad Shafique. (2019). ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining. arXiv (Cornell University). 1–8. 76 indexed citations
12.
Mrázek, Vojtěch, Zdeněk Vašíček, Lukáš Sekanina, Honglan Jiang, & Jie Han. (2018). Scalable Construction of Approximate Multipliers With Formally Guaranteed Worst Case Error. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 26(11). 2572–2576. 40 indexed citations
13.
Mrázek, Vojtěch, et al.. (2018). Evolving boolean functions for fast and efficient randomness testing. Proceedings of the Genetic and Evolutionary Computation Conference. 1302–1309. 1 indexed citations
14.
Mrázek, Vojtěch, Zdeněk Vašíček, & Radek Hrbáček. (2018). Role of circuit representation in evolutionary design of energy‐efficient approximate circuits. IET Computers & Digital Techniques. 12(4). 139–149. 10 indexed citations
15.
Mrázek, Vojtěch, Radek Hrbáček, Zdeněk Vašíček, & Lukáš Sekanina. (2017). EvoApprox8b:  Library of Approximate Adders and Multipliers for Circuit Design and Benchmarking of Approximation Methods. 258–261. 221 indexed citations
16.
Češka, Milan, Jiří Matyáš, Vojtěch Mrázek, et al.. (2017). Approximating complex arithmetic circuits with formal error guarantees: 32-bit multipliers accomplished. 416–423. 35 indexed citations
17.
Sekanina, Lukáš, et al.. (2017). Approximate Circuits in Low-Power Image and Video Processing: The Approximate Median Filter. Radioengineering. 26(3). 623–632. 10 indexed citations
18.
Vašíček, Zdeněk, Vojtěch Mrázek, & Lukáš Sekanina. (2017). Towards low power approximate DCT architecture for HEVC standard. 1576–1581. 18 indexed citations
19.
Mrázek, Vojtěch, Syed Shakib Sarwar, Lukáš Sekanina, Zdeněk Vašíček, & Kaushik Roy. (2016). Design of power-efficient approximate multipliers for approximate artificial neural networks. 1–7. 113 indexed citations
20.
Mrázek, Vojtěch, Zdeněk Vašíček, & Lukáš Sekanina. (2015). Evolutionary Approximation of Software for Embedded Systems. 795–801. 10 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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