Manuel Eggimann

456 total citations
16 papers, 273 citations indexed

About

Manuel Eggimann is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Manuel Eggimann has authored 16 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 6 papers in Biomedical Engineering and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Manuel Eggimann's work include Advanced Memory and Neural Computing (5 papers), CCD and CMOS Imaging Sensors (3 papers) and Advancements in Semiconductor Devices and Circuit Design (3 papers). Manuel Eggimann is often cited by papers focused on Advanced Memory and Neural Computing (5 papers), CCD and CMOS Imaging Sensors (3 papers) and Advancements in Semiconductor Devices and Circuit Design (3 papers). Manuel Eggimann collaborates with scholars based in Switzerland, Italy and United States. Manuel Eggimann's co-authors include Luca Benini, Michele Magno, Philipp Mayer, Stefan Mach, Davide Rossi, Alfio Di Mauro, Abbas Rahimi, Antonio Pullini, Francesco Conti and Giuseppe Tagliavini and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Internet of Things Journal and IEEE Sensors Journal.

In The Last Decade

Manuel Eggimann

16 papers receiving 260 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manuel Eggimann Switzerland 10 148 57 50 44 40 16 273
Injoon Hong South Korea 11 195 1.3× 34 0.6× 163 3.3× 37 0.8× 33 0.8× 37 323
Ryotaro Taniguchi Japan 9 95 0.6× 22 0.4× 95 1.9× 57 1.3× 17 0.4× 64 267
Youchang Kim South Korea 11 322 2.2× 130 2.3× 164 3.3× 31 0.7× 46 1.1× 27 477
Ka-Fai Un Macao 10 176 1.2× 73 1.3× 54 1.1× 8 0.2× 31 0.8× 37 303
Souvik Hazra Germany 11 124 0.8× 148 2.6× 69 1.4× 155 3.5× 24 0.6× 19 374
Luke Everson United States 9 160 1.1× 251 4.4× 40 0.8× 12 0.3× 28 0.7× 21 451
Muhammad Iram Baig Pakistan 10 122 0.8× 35 0.6× 72 1.4× 12 0.3× 11 0.3× 20 307
Shuisheng Lin China 7 67 0.5× 85 1.5× 151 3.0× 14 0.3× 8 0.2× 41 295
S. Palanivel Rajan India 9 92 0.6× 54 0.9× 38 0.8× 7 0.2× 7 0.2× 29 227
A. Rajeswari India 7 115 0.8× 40 0.7× 21 0.4× 7 0.2× 8 0.2× 44 244

Countries citing papers authored by Manuel Eggimann

Since Specialization
Citations

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

Fields of papers citing papers by Manuel Eggimann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manuel Eggimann

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

All Works

16 of 16 papers shown
1.
Zhang, Yichao, Manuel Eggimann, Matheus Cavalcante, et al.. (2025). Occamy: A 432-Core Dual-Chiplet Dual-HBM2E 768-DP-GFLOP/s RISC-V System for 8-to-64-bit Dense and Sparse Computing in 12-nm FinFET. IEEE Journal of Solid-State Circuits. 60(4). 1324–1338. 3 indexed citations
2.
Conti, Francesco, Davide Rossi, Alfio Di Mauro, et al.. (2024). Siracusa: A 16 nm Heterogenous RISC-V SoC for Extended Reality With At-MRAM Neural Engine. IEEE Journal of Solid-State Circuits. 59(7). 2055–2069. 8 indexed citations
3.
Benz, Thomas, Matheus Cavalcante, Manuel Eggimann, et al.. (2024). Occamy: A 432-Core 28.1 DP-GFLOP/s/W 83% FPU Utilization Dual-Chiplet, Dual-HBM2E RISC-V-Based Accelerator for Stencil and Sparse Linear Algebra Computations with 8-to-64-bit Floating-Point Support in 12nm FinFET. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1–2. 10 indexed citations
4.
Rossi, Davide, Angelo Garofalo, Alfio Di Mauro, et al.. (2023). 22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 21–23. 21 indexed citations
5.
Eggimann, Manuel, Alfio Di Mauro, Francesco Conti, et al.. (2023). Siracusa: A Low-Power On-Sensor RISC-V SoC for Extended Reality Visual Processing in 16nm CMOS. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 217–220. 4 indexed citations
6.
Mayer, Philipp, et al.. (2023). Noninvasive Urinary Bladder Volume Estimation With Artifact-Suppressed Bioimpedance Measurements. IEEE Sensors Journal. 24(2). 1633–1643. 6 indexed citations
7.
Eggimann, Manuel, Frank K. Gürkaynak, Luca Benini, et al.. (2022). 64-kB 65-nm GC-eDRAM With Half-Select Support and Parallel Refresh Technique. IEEE Solid-State Circuits Letters. 5. 170–173. 4 indexed citations
8.
Rossi, Davide, Francesco Conti, Manuel Eggimann, et al.. (2021). Vega: A Ten-Core SoC for IoT Endnodes With DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode. IEEE Journal of Solid-State Circuits. 57(1). 127–139. 65 indexed citations
9.
Mayer, Philipp, et al.. (2021). ImpediSense:A long lasting wireless wearable bio-impedance sensor node. Sustainable Computing Informatics and Systems. 30. 100556–100556. 16 indexed citations
10.
Eggimann, Manuel, Abbas Rahimi, & Luca Benini. (2021). A 5 μW Standard Cell Memory-Based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing. IEEE Transactions on Circuits and Systems I Regular Papers. 68(10). 4116–4128. 20 indexed citations
11.
Magno, Michele, et al.. (2021). TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition With Short Range Radars. IEEE Internet of Things Journal. 8(13). 10336–10346. 58 indexed citations
12.
Mayer, Philipp, et al.. (2021). Towards Artefact-Free Bio-Impedance Measurements: Evaluation, Identification and Suppression of Artefacts at Multiple Frequencies. IEEE Sensors Journal. 22(1). 589–600. 6 indexed citations
13.
Ibrahim, Alì, Michele Magno, Manuel Eggimann, et al.. (2019). An Energy Efficient System for Touch Modality Classification in Electronic Skin Applications. CINECA IRIS Institutial Research Information System (University of Genoa). 65. 1–4. 11 indexed citations
14.
Eggimann, Manuel, Stefan Mach, Michele Magno, & Luca Benini. (2019). A RISC-V Based Open Hardware Platform for Always-On Wearable Smart Sensing. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 169–174. 19 indexed citations
15.
Eggimann, Manuel, et al.. (2019). Low Power Embedded Gesture Recognition Using Novel Short-Range Radar Sensors. 1–4. 12 indexed citations
16.
Eggimann, Manuel, et al.. (2015). Automatic Waste Classification using Computer Vision as an Application in Colombian High Schools. 10 (5 .)–10 (5 .). 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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