Alicia Klinefelter

1.4k total citations
19 papers, 1.0k citations indexed

About

Alicia Klinefelter is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Hardware and Architecture. According to data from OpenAlex, Alicia Klinefelter has authored 19 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Electrical and Electronic Engineering, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Hardware and Architecture. Recurrent topics in Alicia Klinefelter's work include Advanced Neural Network Applications (5 papers), Energy Harvesting in Wireless Networks (4 papers) and Advanced Memory and Neural Computing (4 papers). Alicia Klinefelter is often cited by papers focused on Advanced Neural Network Applications (5 papers), Energy Harvesting in Wireless Networks (4 papers) and Advanced Memory and Neural Computing (4 papers). Alicia Klinefelter collaborates with scholars based in United States, France and United Kingdom. Alicia Klinefelter's co-authors include Benton H. Calhoun, Yanqing Zhang, Rangharajan Venkatesan, Brucek Khailany, Aatmesh Shrivastava, James Boley, Yousef Shakhsheer, Matthew Fojtik, William J. Dally and Brian Zimmer and has published in prestigious journals such as Communications of the ACM, IEEE Journal of Solid-State Circuits and IEEE Micro.

In The Last Decade

Alicia Klinefelter

18 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alicia Klinefelter United States 12 760 241 228 220 164 19 1.0k
Tony Tae-Hyoung Kim Singapore 22 1.5k 1.9× 265 1.1× 384 1.7× 67 0.3× 135 0.8× 154 1.7k
Matthew Fojtik United States 18 1.0k 1.4× 148 0.6× 485 2.1× 223 1.0× 73 0.4× 32 1.3k
Qing Dong United States 22 1.4k 1.8× 270 1.1× 523 2.3× 164 0.7× 36 0.2× 68 1.7k
Kyuho Lee South Korea 17 454 0.6× 198 0.8× 67 0.3× 202 0.9× 36 0.2× 51 777
Arnab Raha United States 22 1.1k 1.5× 144 0.6× 502 2.2× 123 0.6× 97 0.6× 89 1.5k
Minsu Choi United States 15 734 1.0× 156 0.6× 185 0.8× 96 0.4× 47 0.3× 137 985
Baibhab Chatterjee United States 17 598 0.8× 629 2.6× 187 0.8× 110 0.5× 24 0.1× 86 1.2k
Dongsuk Jeon South Korea 19 842 1.1× 292 1.2× 100 0.4× 122 0.6× 22 0.1× 58 1.2k
David Fick United States 21 1.4k 1.8× 276 1.1× 651 2.9× 95 0.4× 133 0.8× 31 1.8k
Jose Nunez‐Yanez United Kingdom 17 408 0.5× 63 0.3× 438 1.9× 219 1.0× 35 0.2× 123 979

Countries citing papers authored by Alicia Klinefelter

Since Specialization
Citations

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

Fields of papers citing papers by Alicia Klinefelter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alicia Klinefelter

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

All Works

19 of 19 papers shown
1.
Klinefelter, Alicia, Huichu Liu, Luca Benini, et al.. (2021). SE2: Going Remote: Challenges and Opportunities to Remote Learning, Work, and Collaboration. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 539–540. 1 indexed citations
2.
Charbon, Edoardo, Alicia Klinefelter, Massimo Alioto, et al.. (2021). F4: Electronics for a Quantum World. 525–528. 1 indexed citations
3.
Shao, Yakun Sophia, Rangharajan Venkatesan, Brian Zimmer, et al.. (2021). Simba. Communications of the ACM. 64(6). 107–116. 11 indexed citations
4.
Khailany, Brucek, Haoxing Ren, Steve Dai, et al.. (2020). Accelerating Chip Design With Machine Learning. IEEE Micro. 40(6). 23–32. 36 indexed citations
5.
Zimmer, Brian, Rangharajan Venkatesan, Yakun Sophia Shao, et al.. (2020). A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm. IEEE Journal of Solid-State Circuits. 55(4). 920–932. 82 indexed citations
6.
Fojtik, Matthew, Ben Keller, Alicia Klinefelter, et al.. (2019). A Fine-Grained GALS SoC with Pausible Adaptive Clocking in 16 nm FinFET. 27–35. 8 indexed citations
7.
Shao, Yakun Sophia, Jason Clemons, Rangharajan Venkatesan, et al.. (2019). Simba. 14–27. 248 indexed citations
8.
Venkatesan, Rangharajan, Yakun Sophia Shao, Brian Zimmer, et al.. (2019). A 0.11 PJ/OP, 0.32-128 Tops, Scalable Multi-Chip-Module-Based Deep Neural Network Accelerator Designed with A High-Productivity vlsi Methodology. 1–24. 10 indexed citations
9.
Zimmer, Brian, Rangharajan Venkatesan, Yakun Sophia Shao, et al.. (2019). A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm. C300–C301. 40 indexed citations
10.
Venkatesan, Rangharajan, Yakun Sophia Shao, Jason Clemons, et al.. (2019). MAGNet: A Modular Accelerator Generator for Neural Networks. 1–8. 84 indexed citations
11.
Khailany, Brucek, Rangharajan Venkatesan, Jason Clemons, et al.. (2018). A modular digital VLSI flow for high-productivity SoC design. 1–6. 30 indexed citations
12.
Khailany, Brucek, Rangharajan Venkatesan, Jason Clemons, et al.. (2018). INVITED: A Modular Digital VLSI Flow for High-Productivity SoC Design. 1–6. 16 indexed citations
13.
Roy, Abhishek, Alicia Klinefelter, Xing Chen, et al.. (2015). A 6.45 uW Self-Powered SoC With Integrated Energy-Harvesting Power Management and ULP Asymmetric Radios for Portable Biomedical Systems. IEEE Transactions on Biomedical Circuits and Systems. 9(6). 862–874. 65 indexed citations
14.
Klinefelter, Alicia, Joseph F. Ryan, James Tschanz, & Benton H. Calhoun. (2015). Error-energy analysis of hardware logarithmic approximation methods for low power applications. 2361–2364. 10 indexed citations
15.
Klinefelter, Alicia, Nathan E. Roberts, Yousef Shakhsheer, et al.. (2015). 21.3 A 6.45μW self-powered IoT SoC with integrated energy-harvesting power management and ULP asymmetric radios. 1–3. 90 indexed citations
16.
Klinefelter, Alicia & Benton H. Calhoun. (2014). A reduced-memory FIR filter using approximate coefficients for ultra-low power SoCs. 1–2.
17.
Klinefelter, Alicia, Yanqing Zhang, Brian Otis, & Benton H. Calhoun. (2012). A Programmable 34 nW/Channel Sub-Threshold Signal Band Power Extractor on a Body Sensor Node SoC. IEEE Transactions on Circuits & Systems II Express Briefs. 59(12). 937–941. 11 indexed citations
18.
Zhang, Fan, Yanqing Zhang, Yousef Shakhsheer, et al.. (2012). A batteryless 19μW MICS/ISM-band energy harvesting body area sensor node SoC. 298–300. 45 indexed citations
19.
Zhang, Yanqing, Fan Zhang, Yousef Shakhsheer, et al.. (2012). A Batteryless 19 $\mu$W MICS/ISM-Band Energy Harvesting Body Sensor Node SoC for ExG Applications. IEEE Journal of Solid-State Circuits. 48(1). 199–213. 261 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Tony Tae-Hyoung Kim Breakdown of academic impact, for papers by Matthew Fojtik Breakdown of academic impact, for papers by Qing Dong Breakdown of academic impact, for papers by Kyuho Lee Breakdown of academic impact, for papers by Arnab Raha Breakdown of academic impact, for papers by Minsu Choi Breakdown of academic impact, for papers by Baibhab Chatterjee Breakdown of academic impact, for papers by Dongsuk Jeon Breakdown of academic impact, for papers by David Fick Breakdown of academic impact, for papers by Jose Nunez‐Yanez
Rankless by CCL
2026