Maxime Pelcat

1.3k total citations
48 papers, 251 citations indexed

About

Maxime Pelcat is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Maxime Pelcat has authored 48 papers receiving a total of 251 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Hardware and Architecture, 18 papers in Computer Vision and Pattern Recognition and 16 papers in Electrical and Electronic Engineering. Recurrent topics in Maxime Pelcat's work include Embedded Systems Design Techniques (22 papers), Parallel Computing and Optimization Techniques (13 papers) and Interconnection Networks and Systems (12 papers). Maxime Pelcat is often cited by papers focused on Embedded Systems Design Techniques (22 papers), Parallel Computing and Optimization Techniques (13 papers) and Interconnection Networks and Systems (12 papers). Maxime Pelcat collaborates with scholars based in France, United States and Italy. Maxime Pelcat's co-authors include Daniel Ménard, Jean-François Nezan, Karol Desnos, Slaheddine Aridhi, Wassim Hamidouche, Matthieu Wipliez, Rubén Salvador, Francesca Palumbo, Luigi Raffo and Sebastián López and has published in prestigious journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Lecture notes in computer science and IEEE Transactions on Network and Service Management.

In The Last Decade

Maxime Pelcat

45 papers receiving 246 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxime Pelcat France 9 104 83 74 66 42 48 251
Richard Membarth Germany 10 184 1.8× 99 1.2× 99 1.3× 63 1.0× 13 0.3× 33 304
Aman Arora United States 12 84 0.8× 99 1.2× 43 0.6× 106 1.6× 10 0.2× 49 274
Xiaolang Yan China 10 164 1.6× 146 1.8× 24 0.3× 98 1.5× 9 0.2× 35 268
Zhaoshi Li China 8 187 1.8× 152 1.8× 64 0.9× 86 1.3× 18 0.4× 19 286
Pawan Harish India 6 50 0.5× 43 0.5× 113 1.5× 13 0.2× 21 0.5× 13 196
Ulrich Finkler United States 8 36 0.3× 66 0.8× 45 0.6× 40 0.6× 33 0.8× 21 215
Sandeep Kakde India 10 30 0.3× 113 1.4× 53 0.7× 172 2.6× 13 0.3× 39 283
Zdeněk Martinásek Czechia 8 67 0.6× 67 0.8× 77 1.0× 49 0.7× 39 0.9× 39 243
Oscar Palomar Spain 9 184 1.8× 150 1.8× 38 0.5× 63 1.0× 8 0.2× 48 259
Infall Syafalni Indonesia 7 46 0.4× 49 0.6× 60 0.8× 82 1.2× 10 0.2× 74 224

Countries citing papers authored by Maxime Pelcat

Since Specialization
Citations

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

Fields of papers citing papers by Maxime Pelcat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxime Pelcat

This figure shows the co-authorship network connecting the top 25 collaborators of Maxime Pelcat. A scholar is included among the top collaborators of Maxime Pelcat 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 Maxime Pelcat. Maxime Pelcat 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.
Prévotet, Jean-Christophe, et al.. (2025). Embodied Carbon Footprint of 3D NAND Memories. SPIRE - Sciences Po Institutional REpository. 108–116. 1 indexed citations
2.
Pelcat, Maxime, et al.. (2025). PCBnCO: A Carbon Intensity Model of FR-4 Printed Circuit Boards Based on Company Data. SPIRE - Sciences Po Institutional REpository. 1–8.
3.
Fischer, Viktor, et al.. (2024). Beyond Total Locking: Demonstrating and Measuring Mutual Influence on a RO-Based True Random Number Generator on an FPGA. SPIRE - Sciences Po Institutional REpository. 1–6. 2 indexed citations
4.
Prévotet, Jean-Christophe, et al.. (2024). Streamlined Models of CMOS Image Sensors Carbon Impacts. 250–257. 1 indexed citations
5.
Pelcat, Maxime, et al.. (2023). Automatic CNN Model Partitioning for GPU/FPGA-based Embedded Heterogeneous Accelerators using Geometric Programming. Journal of Signal Processing Systems. 95(10). 1203–1218. 1 indexed citations
6.
Guyet, Thomas, et al.. (2022). SECURE-GEGELATI Always-On Intrusion Detection through GEGELATI Lightweight Tangled Program Graphs. Journal of Signal Processing Systems. 94(7). 753–770. 4 indexed citations
7.
Palumbo, Francesca, et al.. (2021). PathTracer: Understanding Response Time of Signal Processing Applications on Heterogeneous MPSoCs. HAL (Le Centre pour la Communication Scientifique Directe). 6(4). 1–30. 1 indexed citations
8.
Pelcat, Maxime, et al.. (2020). Opendenoising: An Extensible Benchmark for Building Comparative Studies of Image Denoisers. arXiv (Cornell University). 9906. 2648–2652. 2 indexed citations
9.
Desnos, Karol, et al.. (2019). DAMHSE: Programming heterogeneous MPSoCs with hardware acceleration using dataflow-based design space exploration and automated rapid prototyping. Microprocessors and Microsystems. 71. 102882–102882. 6 indexed citations
10.
Pnevmatikatos, Dionisios, et al.. (2019). Embedded Computer Systems: Architectures, Modeling, and Simulation. Lecture notes in computer science. 1 indexed citations
11.
Mercat, Alexandre, et al.. (2018). Probabilistic Approach Versus Machine Learning for One-Shot Quad-Tree Prediction in an Intra HEVC Encoder. Journal of Signal Processing Systems. 91(9). 1021–1037. 4 indexed citations
12.
Pelcat, Maxime, et al.. (2017). Hardware Acceleration of the Tracking Learning Detection (TLD) Algorithm on FPGA. 180–185. 1 indexed citations
13.
Sau, Carlo, Francesca Palumbo, Maxime Pelcat, et al.. (2017). Challenging the Best HEVC Fractional Pixel FPGA Interpolators With Reconfigurable and Multifrequency Approximate Computing. IEEE Embedded Systems Letters. 9(3). 65–68. 12 indexed citations
14.
Mercat, Alexandre, et al.. (2017). Smart search space reduction for approximate computing: A low energy HEVC encoder case study. Journal of Systems Architecture. 80. 56–67. 6 indexed citations
15.
Lazcano, Raquel, Daniel Madroñal, Karol Desnos, et al.. (2016). Parallelism exploitation of a PCA algorithm for hyperspectral images using RVC-CAL. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10007. 100070H–100070H. 2 indexed citations
16.
Desnos, Karol, Maxime Pelcat, Jean-François Nezan, & Slaheddine Aridhi. (2016). On Memory Reuse Between Inputs and Outputs of Dataflow Actors. ACM Transactions on Embedded Computing Systems. 15(2). 1–25. 6 indexed citations
17.
Pelcat, Maxime, et al.. (2015). Energy-Awareness and Performance Management with Parallel Dataflow Applications. Journal of Signal Processing Systems. 87(1). 33–48. 5 indexed citations
18.
Zhang, Jinglin, et al.. (2013). Real-time GPU-based local stereo matching method. 209–214. 8 indexed citations
19.
Pelcat, Maxime, et al.. (2012). Physical Layer Multi-Core Prototyping. Lecture notes in electrical engineering. 10 indexed citations
20.
Pelcat, Maxime, et al.. (2008). Optimization of automatically generated multi-core code for the LTE RACH-PD algorithm. HAL (Le Centre pour la Communication Scientifique Directe). 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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