Georg Ellguth

669 total citations
24 papers, 370 citations indexed

About

Georg Ellguth is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Georg Ellguth has authored 24 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Electrical and Electronic Engineering, 9 papers in Computer Networks and Communications and 8 papers in Hardware and Architecture. Recurrent topics in Georg Ellguth's work include Advanced Memory and Neural Computing (8 papers), Interconnection Networks and Systems (7 papers) and Low-power high-performance VLSI design (6 papers). Georg Ellguth is often cited by papers focused on Advanced Memory and Neural Computing (8 papers), Interconnection Networks and Systems (7 papers) and Low-power high-performance VLSI design (6 papers). Georg Ellguth collaborates with scholars based in Germany, United Kingdom and Switzerland. Georg Ellguth's co-authors include Sebastian Höppner, René Schüffny, Stefan Scholze, Holger Eisenreich, Christian Mayr, Dennis Walter, Johannes Partzsch, Marko Noack, Stephan Henker and Gerhard Fettweis and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Frontiers in Neuroscience and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Georg Ellguth

23 papers receiving 362 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Georg Ellguth Germany 12 324 101 84 68 54 24 370
Seamus Cawley Ireland 11 322 1.0× 185 1.8× 77 0.9× 66 1.0× 36 0.7× 20 390
Stefan Scholze Germany 12 345 1.1× 166 1.6× 143 1.7× 37 0.5× 46 0.9× 32 416
Bo Marr United States 6 438 1.4× 90 0.9× 92 1.1× 27 0.4× 54 1.0× 11 490
Brian Degnan United States 10 376 1.2× 44 0.4× 55 0.7× 23 0.3× 94 1.7× 28 420
Dennis Walter Germany 11 255 0.8× 45 0.4× 42 0.5× 59 0.9× 63 1.2× 25 332
Xiaoxin Cui China 11 473 1.5× 87 0.9× 78 0.9× 49 0.7× 24 0.4× 134 577
Jan Stuijt Netherlands 10 246 0.8× 81 0.8× 65 0.8× 24 0.4× 54 1.0× 21 293
Holger Eisenreich Germany 11 240 0.7× 35 0.3× 39 0.5× 66 1.0× 45 0.8× 22 275
Mark E. Dean United States 11 415 1.3× 45 0.4× 92 1.1× 90 1.3× 33 0.6× 24 464

Countries citing papers authored by Georg Ellguth

Since Specialization
Citations

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

Fields of papers citing papers by Georg Ellguth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Georg Ellguth

This figure shows the co-authorship network connecting the top 25 collaborators of Georg Ellguth. A scholar is included among the top collaborators of Georg Ellguth 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 Georg Ellguth. Georg Ellguth 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.
Guo, Liyuan, Stefan Scholze, M. Berthel, et al.. (2024). 68-channel neural signal processing system-on-chip with integrated feature extraction, compression, and hardware accelerators for neuroprosthetics in 22 nm FDSOI. Frontiers in Neuroscience. 18. 1432750–1432750. 3 indexed citations
2.
Scholze, Stefan, et al.. (2022). A 16-Channel Fully Configurable Neural SoC With 1.52 $\mu$W/Ch Signal Acquisition, 2.79 $\mu$W/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI. IEEE Transactions on Biomedical Circuits and Systems. 16(1). 94–107. 32 indexed citations
3.
Höppner, Sebastian, Bernhard Vogginger, Stefan Scholze, et al.. (2019). Dynamic Power Management for Neuromorphic Many-Core Systems. IEEE Transactions on Circuits and Systems I Regular Papers. 66(8). 2973–2986. 15 indexed citations
4.
Höppner, Sebastian, Bernhard Vogginger, Johannes Partzsch, et al.. (2017). Dynamic voltage and frequency scaling for neuromorphic many-core systems. Research Explorer (The University of Manchester). 1–4. 10 indexed citations
5.
Arnold, Oliver, Stefan Scholze, Sebastian Höppner, et al.. (2016). A database accelerator for energy-efficient query processing and optimization. Qucosa (Saxon State and University Library Dresden). 1–5. 8 indexed citations
6.
Noack, Marko, et al.. (2015). Switched-capacitor realization of presynaptic short-term-plasticity and stop-learning synapses in 28 nm CMOS. Frontiers in Neuroscience. 9. 10–10. 28 indexed citations
7.
Höppner, Sebastian, Dennis Walter, Thomas Hocker, et al.. (2015). An Energy Efficient Multi-Gbit/s NoC Transceiver Architecture With Combined AC/DC Drivers and Stoppable Clocking in 65 nm and 28 nm CMOS. IEEE Journal of Solid-State Circuits. 50(3). 749–762. 17 indexed citations
8.
Mayr, Christian, Johannes Partzsch, Marko Noack, et al.. (2015). A Biological-Realtime Neuromorphic System in 28 nm CMOS Using Low-Leakage Switched Capacitor Circuits. IEEE Transactions on Biomedical Circuits and Systems. 10(1). 243–254. 73 indexed citations
9.
Höppner, Sebastian, Holger Eisenreich, Georg Ellguth, et al.. (2014). A compact on-chip IR-drop measurement system in 28 nm CMOS technology. 1219–1222. 5 indexed citations
10.
11.
Höppner, Sebastian, et al.. (2013). A Fast-Locking ADPLL With Instantaneous Restart Capability in 28-nm CMOS Technology. IEEE Transactions on Circuits & Systems II Express Briefs. 60(11). 741–745. 36 indexed citations
12.
Matúš, Emil, Gerhard Fettweis, Holger Eisenreich, et al.. (2012). A 335Mb/s 3.9mm<sup>2</sup> 65nm CMOS flexible MIMO detection-decoding engine achieving 4G wireless data rates. 216–218. 11 indexed citations
13.
Höppner, Sebastian, et al.. (2012). A power management architecture for fast per-core DVFS in heterogeneous MPSoCs. 261–264. 16 indexed citations
14.
Walter, Dennis, Sebastian Höppner, Holger Eisenreich, et al.. (2012). A source-synchronous 90Gb/s capacitively driven serial on-chip link over 6mm in 65nm CMOS. 180–182. 25 indexed citations
15.
Höppner, Sebastian, Dennis Walter, Georg Ellguth, & René Schüffny. (2012). On-Chip Measurement and Compensation of Timing Imbalances in High-Speed Serial NoC Links. RePEc: Research Papers in Economics. 3(4). 42–56. 1 indexed citations
16.
Höppner, Sebastian, Dennis Walter, Georg Ellguth, & René Schüffny. (2011). Mismatch characterization of high-speed NoC links using asynchronous sub-sampling. 112–115. 2 indexed citations
17.
Scholze, Stefan, Holger Eisenreich, Sebastian Höppner, et al.. (2011). A 32 GBit/s communication SoC for a waferscale neuromorphic system. Integration. 45(1). 61–75. 30 indexed citations
18.
Berthold, J., et al.. (2010). Low power design of the X-GOLD® SDR 20 baseband processor. Design, Automation, and Test in Europe. 792–793. 1 indexed citations
19.
Raab, W., et al.. (2010). Low power design of the X-GOLD<sup>&#x00AE;</sup> SDR 20 baseband processor. 792–793. 1 indexed citations
20.
Ellguth, Georg, et al.. (2009). Current conveyor based amplifier and adaptive buffer for use in an analog frontend. 5. 9–12. 1 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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