Daniel Große

3.0k citations
187 papers · 2.3k · h-index 27

Impact in

Papers in

Daniel Große

175 papers receiving 2.2k citations

Peers

Daniel Große
Comparison fields: 5 of 70
  • Hardware and Architecture 878
  • Software 397
  • Computational Theory and Mathematics 1.1k
  • Artificial Intelligence 729
  • Electrical and Electronic Engineering 808
Replace Chengyong Wu with:
Chengyong Wu China
Williams United States
Christian Pilato Italy
A. Thompson United Kingdom
Ronak Singhal United States
Per Hammarlund Sweden
Christos D. Antonopoulos Greece
Luis Valencia–Cabrera Spain
Shan Gai China
Frank Sill Torres Germany
Daniel Große relative to Chengyong Wu China Chengyong Wu's profile →
Citations per field
00.5×10×12.6×
Chengyong Wu · 1×
Citations per year

Countries citing papers authored by Daniel Große

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Große

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Große, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Große Line = papers co-authored together Daniel Große links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 187 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009132
2 198066
3 200962
4 201057
5 201655
6 201654
7 201949
8 202046
9 201844
10 200744
11 200943
12 200941
13 201841
14 200340
15 201940
16 201339
17 200838
18 200738
19 200838
20 201336

About Daniel Große

Daniel Große is a scholar working on Hardware and Architecture, Computational Theory and Mathematics, Electrical and Electronic Engineering, Artificial Intelligence and Software, having authored 187 papers that have together received 2.3k indexed citations. Recurring topics across this work include Formal Methods in Verification (70 papers), Embedded Systems Design Techniques (61 papers), VLSI and Analog Circuit Testing (51 papers), Software Testing and Debugging Techniques (34 papers), Radiation Effects in Electronics (28 papers), Low-power high-performance VLSI design (18 papers), Parallel Computing and Optimization Techniques (18 papers) and Logic, programming, and type systems (14 papers). The work is most often cited by research in Hardware and Architecture (878 citations), Software (397 citations), Computational Theory and Mathematics (1.1k citations), Artificial Intelligence (729 citations) and Electrical and Electronic Engineering (808 citations). Daniel Große has collaborated with scholars based in Germany, Austria and Canada. Frequent co-authors include Rolf Drechsler, Robert Wille, Hoang M. Le, Gerhard W. Dueck, Vladimir Herdt, Bruce J. Turner, Mathias Soeken, Ulrich Kühne, Alireza Mahzoon and Görschwin Fey. Their work appears in journals such as Fusion Engineering and Design, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Journal of Systems Architecture, Evolution and International Journal on Software Tools for Technology Transfer.

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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