Vivek Parmar

1.8k total citations
36 papers, 438 citations indexed

About

Vivek Parmar is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Vivek Parmar has authored 36 papers receiving a total of 438 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Electrical and Electronic Engineering, 13 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Vivek Parmar's work include Advanced Memory and Neural Computing (28 papers), Ferroelectric and Negative Capacitance Devices (23 papers) and Machine Learning and ELM (7 papers). Vivek Parmar is often cited by papers focused on Advanced Memory and Neural Computing (28 papers), Ferroelectric and Negative Capacitance Devices (23 papers) and Machine Learning and ELM (7 papers). Vivek Parmar collaborates with scholars based in India, France and Taiwan. Vivek Parmar's co-authors include Manan Suri, Sandeep Kaur Kingra, Tuo‐Hung Hou, Che‐Chia Chang, Boris Hudec, Earl E. Swartzlander, Shubham Sahay, Fabien Alibart, Damien Querlioz and Alessandro Bricalli and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Scientific Reports.

In The Last Decade

Vivek Parmar

33 papers receiving 431 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vivek Parmar India 10 252 67 43 42 36 36 438
Robert Karam United States 12 297 1.2× 120 1.8× 268 6.2× 76 1.8× 18 0.5× 55 544
Zia Abbas India 12 278 1.1× 20 0.3× 49 1.1× 10 0.2× 12 0.3× 64 385
Rushi Patel United States 9 90 0.4× 60 0.9× 81 1.9× 9 0.2× 75 2.1× 37 273
Motonobu Hattori Japan 13 104 0.4× 251 3.7× 9 0.2× 24 0.6× 56 1.6× 52 419
Gagandeep Singh India 13 140 0.6× 104 1.6× 138 3.2× 9 0.2× 34 0.9× 48 499
Sandeep Kaur Kingra India 6 86 0.3× 22 0.3× 6 0.1× 19 0.5× 12 0.3× 17 247
Henry M. D. Ip United Kingdom 7 114 0.5× 34 0.5× 4 0.1× 25 0.6× 23 0.6× 16 355
Xiling Yin China 7 410 1.6× 87 1.3× 15 0.3× 135 3.2× 53 1.5× 10 452
Tae Sun Kim South Korea 11 36 0.1× 17 0.3× 15 0.3× 27 0.6× 18 0.5× 42 434

Countries citing papers authored by Vivek Parmar

Since Specialization
Citations

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

Fields of papers citing papers by Vivek Parmar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vivek Parmar

This figure shows the co-authorship network connecting the top 25 collaborators of Vivek Parmar. A scholar is included among the top collaborators of Vivek Parmar 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 Vivek Parmar. Vivek Parmar 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.
Parmar, Vivek, et al.. (2023). Analysis of VMM computation strategies to implement BNN applications on RRAM arrays. SHILAP Revista de lepidopterología. 1(2). 2 indexed citations
2.
Parmar, Vivek, Franz Müller, Sandeep Kaur Kingra, et al.. (2023). Demonstration of Differential Mode Ferroelectric Field‐Effect Transistor Array‐Based in‐Memory Computing Macro for Realizing Multiprecision Mixed‐Signal Artificial Intelligence Accelerator. SHILAP Revista de lepidopterología. 5(6). 12 indexed citations
3.
Parmar, Vivek, Sandeep Kaur Kingra, Syed Shakib Sarwar, et al.. (2023). Fully-Binarized Distance Computation based On-device Few-Shot Learning for XR applications. 30. 4502–4508. 1 indexed citations
4.
Parmar, Vivek, Sandeep Kaur Kingra, Deepak Verma, et al.. (2023). Demonstration of SMT-reflow Immune and SCA-resilient PUF on 28nm RRAM device array. SPIRE - Sciences Po Institutional REpository. 1–4.
5.
6.
Parmar, Vivek, Franz Müller, Sandeep Kaur Kingra, et al.. (2023). Demonstration of Differential Mode FeFET-Array for multi-precision storage and IMC applications. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1–2. 4 indexed citations
7.
Parmar, Vivek, Bogdan Penkovsky, Damien Querlioz, & Manan Suri. (2022). Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing. Frontiers in Neuroscience. 15. 781786–781786. 4 indexed citations
8.
Kingra, Sandeep Kaur, et al.. (2022). Dual-configuration in-memory computing bitcells using SiOx RRAM for binary neural networks. Applied Physics Letters. 120(3). 17 indexed citations
9.
Kingra, Sandeep Kaur, Vivek Parmar, Manoj Kumar Sharma, & Manan Suri. (2022). Time-Multiplexed In-Memory Computation Scheme for Mapping Quantized Neural Networks on Hybrid CMOS-OxRAM Building Blocks. IEEE Transactions on Nanotechnology. 21. 406–412. 2 indexed citations
10.
Parmar, Vivek, et al.. (2021). MRAM-based BER resilient Quantized edge-AI Networks for Harsh Industrial Conditions. 1–4. 2 indexed citations
11.
Kingra, Sandeep Kaur, Vivek Parmar, Che‐Chia Chang, et al.. (2020). SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices. Scientific Reports. 10(1). 2567–2567. 169 indexed citations
12.
Parmar, Vivek & Manan Suri. (2020). A Hybrid CMOS-Memristive Approach to Designing Deep Generative Models. IEEE Transactions on Neural Networks and Learning Systems. 32(6). 2790–2796. 1 indexed citations
13.
Bennett, Christopher H., Vivek Parmar, Laurie E. Calvet, et al.. (2019). Contrasting Advantages of Learning With Random Weights and Backpropagation in Non-Volatile Memory Neural Networks. IEEE Access. 7. 73938–73953. 4 indexed citations
14.
Parmar, Vivek, Jung-Ho Ahn, & Manan Suri. (2019). Hyperspectral Image Classification for Remote Sensing Using Low-Power Neuromorphic Hardware. 1–7. 2 indexed citations
15.
Sharma, Manoj Kumar, Umesh Chandra Lohani, Vivek Parmar, & Manan Suri. (2019). Design of an Optimized CMOS ELM Accelerator. 213. 443–447. 1 indexed citations
16.
Parmar, Vivek, et al.. (2018). Hybrid methodology for credit card anomaly detection. International journal of advance research, ideas and innovations in technology. 4(2). 2293–2295. 1 indexed citations
17.
Parmar, Vivek, Sandeep Kaur Kingra, & Manan Suri. (2018). Short-term plasticity circuit device exploration in the MASTISK neuromorphic framework. Journal of Physics D Applied Physics. 51(45). 454004–454004. 5 indexed citations
18.
Parmar, Vivek, et al.. (2018). MASTISK: Simulation Framework For Design Exploration Of Neuromorphic Hardware. 1–9. 2 indexed citations
19.
Kumar, Amar, Manan Suri, Vivek Parmar, Nicolas Locatelli, & Damien Querlioz. (2016). An energy-efficient hybrid (CMOS-MTJ) TCAM using stochastic writes for approximate computing. 38. 1–5. 3 indexed citations
20.
Suri, Manan & Vivek Parmar. (2015). Exploiting Intrinsic Variability of Filamentary Resistive Memory for Extreme Learning Machine Architectures. IEEE Transactions on Nanotechnology. 14(6). 963–968. 23 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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