Dayane Reis

986 total citations
38 papers, 723 citations indexed

About

Dayane Reis is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Materials Chemistry. According to data from OpenAlex, Dayane Reis has authored 38 papers receiving a total of 723 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Electrical and Electronic Engineering, 9 papers in Artificial Intelligence and 6 papers in Materials Chemistry. Recurrent topics in Dayane Reis's work include Advanced Memory and Neural Computing (31 papers), Ferroelectric and Negative Capacitance Devices (29 papers) and Semiconductor materials and devices (15 papers). Dayane Reis is often cited by papers focused on Advanced Memory and Neural Computing (31 papers), Ferroelectric and Negative Capacitance Devices (29 papers) and Semiconductor materials and devices (15 papers). Dayane Reis collaborates with scholars based in United States, China and Philippines. Dayane Reis's co-authors include Xiaobo Sharon Hu, Michael Niemier, Joerg Appenzeller, Peng Wu, Xunzhao Yin, Kai Ni, Suman Datta, Ann Franchesca Laguna, Cheng Zhuo and Di Gao and has published in prestigious journals such as IEEE Transactions on Electron Devices, IEEE Transactions on Computers and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Dayane Reis

34 papers receiving 707 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dayane Reis United States	13	617	155	107	87	74	38	723
Xiaoyong Xue China	15	587 1.0×	154 1.0×	80 0.7×	123 1.4×	21 0.3×	86	716
Harish M. Kittur India	11	498 0.8×	91 0.6×	58 0.5×	137 1.6×	166 2.2×	66	656
Chrong-Jung Lin Taiwan	10	538 0.9×	85 0.5×	44 0.4×	91 1.0×	28 0.4×	33	581
E. Camerlenghi Italy	7	506 0.8×	152 1.0×	63 0.6×	78 0.9×	63 0.9×	14	703
Stefan Cosemans Belgium	20	1.1k 1.7×	94 0.6×	76 0.7×	108 1.2×	16 0.2×	83	1.1k
Heiner Giefers Switzerland	10	441 0.7×	76 0.5×	139 1.3×	107 1.2×	23 0.3×	28	561
Xi Jin China	11	525 0.9×	204 1.3×	96 0.9×	43 0.5×	14 0.2×	49	721
Je-Min Hung Taiwan	12	744 1.2×	42 0.3×	138 1.3×	123 1.4×	21 0.3×	15	815
Keh-Chung Wang Taiwan	13	493 0.8×	36 0.2×	85 0.8×	57 0.7×	24 0.3×	74	565
Amogh Agrawal United States	15	673 1.1×	25 0.2×	136 1.3×	132 1.5×	27 0.4×	32	774

Countries citing papers authored by Dayane Reis

Since Specialization

Citations

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

Fields of papers citing papers by Dayane Reis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dayane Reis

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

All Works

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20 of 20 papers shown

Geng, Haoran, et al.. (2025). Shared-PIM: Enabling Concurrent Computation and Data Flow for Faster Processing-in-DRAM. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(11). 4395–4404.

Bhanja, Sanjukta, et al.. (2024). The Impact of Device Technologies on the Design of Non-Volatile Content Addressable Memories. 513–516. 1 indexed citations

Reis, Dayane, et al.. (2024). AFeCAM: An Energy Efficient Analog 1FeFET Content Addressable Memory. 541–545. 1 indexed citations

Laguna, Ann Franchesca, et al.. (2023). Invited Paper: Algorithm/Hardware Co-Design for Few-Shot Learning at the Edge. 1 indexed citations

Reis, Dayane, et al.. (2023). Accelerating Finite-Field and Torus Fully Homomorphic Encryption via Compute-Enabled (S)RAM. IEEE Transactions on Computers. 73(10). 2449–2462. 1 indexed citations

Reis, Dayane, et al.. (2022). Ferroelectric FET Configurable Memory Arrays and Their Applications. 2022 International Electron Devices Meeting (IEDM). 21.5.1–21.5.4. 2 indexed citations

Laguna, Ann Franchesca, et al.. (2022). Design of a Compact Spin-Orbit-Torque-Based Ternary Content Addressable Memory. IEEE Transactions on Electron Devices. 70(2). 506–513. 12 indexed citations

Laguna, Ann Franchesca, et al.. (2022). iMARS. Proceedings of the 59th ACM/IEEE Design Automation Conference. 463–468. 9 indexed citations

Reis, Dayane. (2022). Reconfigurable logic-in-memory. Nature Electronics. 5(11). 713–714. 1 indexed citations

10.

Sheng, Yi, et al.. (2022). Hardware-aware Automated Architecture Search for Brain-inspired Hyperdimensional Computing. 352–357. 4 indexed citations

11.

Reis, Dayane, Michael Niemier, & Xiaobo Sharon Hu. (2021). The Implications of Ferroelectric FET Device Models to the Design of Computing-in-Memory Architectures. Journal of Integrated Circuits and Systems. 16(1). 1–8. 2 indexed citations

12.

Reis, Dayane, Ann Franchesca Laguna, Michael Niemier, & Xiaobo Sharon Hu. (2021). Exploiting FeFETs via Cross-Layer Design from In-memory Computing Circuits to Meta-Learning Applications. 306–311. 2 indexed citations

13.

Gao, Di, Dayane Reis, Xiaobo Sharon Hu, & Cheng Zhuo. (2020). Eva-CiM: A System-Level Performance and Energy Evaluation Framework for Computing-in-Memory Architectures. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39(12). 5011–5024. 30 indexed citations

14.

Brooks, D., Martin M. Frank, Tayfun Gokmen, et al.. (2020). Emerging Neural Workloads and Their Impact on Hardware. HAL (Le Centre pour la Communication Scientifique Directe). 1462–1471. 3 indexed citations

15.

Gao, Di, Dayane Reis, Xiaobo Sharon Hu, & Cheng Zhuo. (2019). Eva-CiM: A System-Level Energy Evaluation Framework for Computing-in-Memory Architectures.. arXiv (Cornell University). 2 indexed citations

16.

Laguna, Ann Franchesca, Xunzhao Yin, Dayane Reis, Michael Niemier, & Xiaobo Sharon Hu. (2019). Ferroelectric FET Based In-Memory Computing for Few-Shot Learning. 373–378. 29 indexed citations

17.

Angizi, Shaahin, Zhezhi He, Dayane Reis, et al.. (2019). Accelerating Deep Neural Networks in Processing-in-Memory Platforms: Analog or Digital Approach?. 29 indexed citations

18.

Yin, Xunzhao, Dayane Reis, Michael Niemier, & Xiaobo Sharon Hu. (2019). Ferroelectric FET Based TCAM Designs for Energy Efficient Computing. 437–442. 6 indexed citations

19.

Reis, Dayane, Michael Niemier, & Xiaobo Sharon Hu. (2019). A Computing-in-Memory Engine for Searching on Homomorphically Encrypted Data. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 5(2). 123–131. 18 indexed citations

20.

Reis, Dayane, Michael Niemier, & Xiaobo Sharon Hu. (2018). Computing in memory with FeFETs. 1–6. 76 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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