Priyadarshini Panda

6.1k total citations · 2 hit papers
92 papers, 3.6k citations indexed

About

Priyadarshini Panda is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Priyadarshini Panda has authored 92 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Electrical and Electronic Engineering, 44 papers in Artificial Intelligence and 29 papers in Cognitive Neuroscience. Recurrent topics in Priyadarshini Panda's work include Advanced Memory and Neural Computing (64 papers), Ferroelectric and Negative Capacitance Devices (45 papers) and Neural dynamics and brain function (28 papers). Priyadarshini Panda is often cited by papers focused on Advanced Memory and Neural Computing (64 papers), Ferroelectric and Negative Capacitance Devices (45 papers) and Neural dynamics and brain function (28 papers). Priyadarshini Panda collaborates with scholars based in United States, India and Lebanon. Priyadarshini Panda's co-authors include Kaushik Roy, Akhilesh Jaiswal, Gopalakrishnan Srinivasan, Chankyu Lee, Youngeun Kim, Youngeun Kim, Abhronil Sengupta, Syed Shakib Sarwar, Aayush Ankit and Sungeun Hong and has published in prestigious journals such as Nature, Nature Communications and Scientific Reports.

In The Last Decade

Priyadarshini Panda

82 papers receiving 3.5k citations

Hit Papers

Towards spike-based machine intelligence with neuromorphi... 2019 2026 2021 2023 2019 2020 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Towards spike-based machine intelligence with neuromorphic computing
    2019 1.3k citations Kaushik Roy, Priyadarshini Panda et al. profile →
  2. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures
    2020 292 citations Priyadarshini Panda, Gopalakrishnan Srinivasan et al. Frontiers in Neuroscience profile →

Peers

Priyadarshini Panda
Rathinakumar Appuswamy United States
Steven K. Esser United States
Brian Taba United States
Nabil Imam United States
Filipp Akopyan United States
Jun Sawada United States
Rodrigo Alvarez-Icaza United States
Carmelo di Nolfo United States
Arnon Amir United States
Yutaka Nakamura Japan
Rathinakumar Appuswamy United States
Priyadarshini Panda
Citations per year, relative to Priyadarshini Panda Priyadarshini Panda (= 1×) peers Rathinakumar Appuswamy

Countries citing papers authored by Priyadarshini Panda

Since Specialization
Citations

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

Fields of papers citing papers by Priyadarshini Panda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Priyadarshini Panda

This figure shows the co-authorship network connecting the top 25 collaborators of Priyadarshini Panda. A scholar is included among the top collaborators of Priyadarshini Panda 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 Priyadarshini Panda. Priyadarshini Panda 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.
Wu, Chenxi, et al.. (2025). Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning. SIAM Journal on Scientific Computing. 47(3). C559–C577.
2.
Kim, Youngeun, et al.. (2024). Do we really need a large number of visual prompts?. Neural Networks. 177. 106390–106390. 4 indexed citations
3.
Kim, Youngeun, et al.. (2024). RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(2). 2101–2111. 1 indexed citations
4.
Kim, Youngeun, et al.. (2024). When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design. Applied Physics Reviews. 11(3). 2 indexed citations
5.
Kim, Youngeun, et al.. (2024). Workload-Balanced Pruning for Sparse Spiking Neural Networks. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(4). 2897–2907. 13 indexed citations
6.
Kim, Youngeun, et al.. (2024). TReX- Reusing Vision Transformer’s Attention for Efficient Xbar-Based Computing. IEEE Transactions on Emerging Topics in Computing. 13(3). 686–697.
7.
8.
Panda, Priyadarshini, et al.. (2024). ClipFormer : Key–Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(2). 592–601. 2 indexed citations
9.
Panda, Priyadarshini, et al.. (2024). MCAIMem: A Mixed SRAM and eDRAM Cell for Area and Energy-Efficient On-Chip AI Memory. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 32(11). 2023–2036. 1 indexed citations
10.
Kim, Youngeun, et al.. (2023). Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks. Frontiers in Neuroscience. 17. 1230002–1230002. 6 indexed citations
11.
Panda, Priyadarshini, et al.. (2023). HyDe: A Hybrid PCM/FeFET/SRAM Device-Search for Optimizing Area and Energy-Efficiencies in Analog IMC Platforms. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 13(4). 1073–1082. 5 indexed citations
12.
Panda, Priyadarshini, et al.. (2023). XploreNAS : Explore Adversarially Robust and Hardware-efficient Neural Architectures for Non-ideal Xbars. ACM Transactions on Embedded Computing Systems. 22(4). 1–17. 1 indexed citations
13.
Kim, Youngeun, et al.. (2023). Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing. arXiv (Cornell University). 619–624. 9 indexed citations
14.
Kim, Youngeun, et al.. (2022). SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42(6). 1926–1938. 36 indexed citations
15.
Panda, Priyadarshini, et al.. (2022). SwitchX: Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars. ACM Transactions on Design Automation of Electronic Systems. 28(4). 1–21. 7 indexed citations
16.
Rathi, Nitin, Indranil Chakraborty, Adarsh Kumar Kosta, et al.. (2022). Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware. ACM Computing Surveys. 55(12). 1–49. 106 indexed citations
17.
Kim, Youngeun, et al.. (2021). NEAT: Nonlinearity Aware Training for Accurate, Energy-Efficient, and Robust Implementation of Neural Networks on 1T-1R Crossbars. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41(8). 2625–2637. 22 indexed citations
18.
Dutta, Sourav, Atanu Saha, Priyadarshini Panda, et al.. (2019). Biologically Plausible Ferroelectric Quasi-Leaky Integrate and Fire Neuron. T140–T141. 22 indexed citations
19.
Chatterjee, Baibhab, Priyadarshini Panda, Shovan Maity, et al.. (2019). Exploiting Inherent Error Resiliency of Deep Neural Networks to Achieve Extreme Energy Efficiency Through Mixed-Signal Neurons. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 27(6). 1365–1377. 12 indexed citations
20.
Srinivasan, Gopalakrishnan, et al.. (2019). Structured Learning for Action Recognition in Videos. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9(3). 475–484.

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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