Sujan K. Gonugondla

1.0k total citations
25 papers, 737 citations indexed

About

Sujan K. Gonugondla is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sujan K. Gonugondla has authored 25 papers receiving a total of 737 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 12 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sujan K. Gonugondla's work include Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and Low-power high-performance VLSI design (6 papers). Sujan K. Gonugondla is often cited by papers focused on Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and Low-power high-performance VLSI design (6 papers). Sujan K. Gonugondla collaborates with scholars based in United States, Germany and Egypt. Sujan K. Gonugondla's co-authors include Naresh R. Shanbhag, Mingu Kang, Ameya D. Patil, Sungmin Lim, Charbel Sakr, Haocheng Hua, Jungwook Choi, Vikram Adve, Nam Sung Kim and Min-Sun Keel and has published in prestigious journals such as Proceedings of the IEEE, IEEE Journal of Solid-State Circuits and IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

In The Last Decade

Sujan K. Gonugondla

21 papers receiving 725 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sujan K. Gonugondla United States 13 640 165 140 103 72 25 737
En-Yu Yang United States 8 494 0.8× 151 0.9× 91 0.7× 131 1.3× 58 0.8× 15 620
Hongyang Jia China 11 668 1.0× 218 1.3× 157 1.1× 99 1.0× 91 1.3× 36 826
Yinqi Tang United States 9 614 1.0× 195 1.2× 118 0.8× 112 1.1× 71 1.0× 12 732
Behnam Khaleghi United States 16 577 0.9× 185 1.1× 218 1.6× 70 0.7× 125 1.7× 51 744
Hyunjoon Kim Singapore 15 407 0.6× 221 1.3× 115 0.8× 134 1.3× 90 1.3× 23 595
Muya Chang United States 14 392 0.6× 128 0.8× 97 0.7× 60 0.6× 31 0.4× 35 502
Xiaoxin Cui China 11 473 0.7× 134 0.8× 106 0.8× 41 0.4× 49 0.7× 134 577
H. Ekin Sumbul United States 10 346 0.5× 118 0.7× 184 1.3× 88 0.9× 135 1.9× 24 522
Mahdi Nazm Bojnordi United States 12 364 0.6× 118 0.7× 199 1.4× 85 0.8× 169 2.3× 42 529
Shuangchen Li China 9 573 0.9× 119 0.7× 156 1.1× 41 0.4× 111 1.5× 25 682

Countries citing papers authored by Sujan K. Gonugondla

Since Specialization
Citations

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

Fields of papers citing papers by Sujan K. Gonugondla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sujan K. Gonugondla. 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 Sujan K. Gonugondla. The network helps show where Sujan K. Gonugondla may publish in the future.

Co-authorship network of co-authors of Sujan K. Gonugondla

This figure shows the co-authorship network connecting the top 25 collaborators of Sujan K. Gonugondla. A scholar is included among the top collaborators of Sujan K. Gonugondla 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 Sujan K. Gonugondla. Sujan K. Gonugondla 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.
Athiwaratkun, Ben, Shiqi Wang, Mingyue Shang, et al.. (2024). Token Alignment via Character Matching for Subword Completion. 15725–15738.
2.
Qian, Haifeng, Sujan K. Gonugondla, Mingyue Shang, et al.. (2024). BASS: Batched Attention-optimized Speculative Sampling. 8214–8224.
3.
Wei, Xiaokai, Sujan K. Gonugondla, Wasi Uddin Ahmad, et al.. (2023). Towards Greener Yet Powerful Code Generation via Quantization: An Empirical Study. 224–236. 14 indexed citations
4.
5.
Gonugondla, Sujan K. & Naresh R. Shanbhag. (2022). IMPQ: Reduced Complexity Neural Networks Via Granular Precision Assignment. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 66–70.
6.
Kang, Mingu, Sujan K. Gonugondla, & Naresh R. Shanbhag. (2020). Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing. Proceedings of the IEEE. 108(12). 2251–2275. 31 indexed citations
7.
Kang, Mingu, Sujan K. Gonugondla, & Naresh R. Shanbhag. (2020). Deep In-memory Architectures for Machine Learning. 6 indexed citations
8.
Gonugondla, Sujan K., Ameya D. Patil, & Naresh R. Shanbhag. (2020). SWIPE. 1–9. 5 indexed citations
9.
Gonugondla, Sujan K., et al.. (2019). Adaptive Filtering in In-Memory-Based Architectures. 784–788. 1 indexed citations
10.
Gonugondla, Sujan K., Mingu Kang, & Naresh R. Shanbhag. (2018). A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training. 490–492. 157 indexed citations
11.
Kang, Mingu, Sujan K. Gonugondla, Sungmin Lim, & Naresh R. Shanbhag. (2018). A 19.4-nJ/Decision, 364-K Decisions/s, In-Memory Random Forest Multi-Class Inference Accelerator. IEEE Journal of Solid-State Circuits. 53(7). 2126–2135. 44 indexed citations
12.
Kang, Mingu, Sungmin Lim, Sujan K. Gonugondla, & Naresh R. Shanbhag. (2018). An In-Memory VLSI Architecture for Convolutional Neural Networks. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 8(3). 494–505. 41 indexed citations
13.
Gonugondla, Sujan K., Mingu Kang, & Naresh R. Shanbhag. (2018). A Variation-Tolerant In-Memory Machine Learning Classifier via On-Chip Training. IEEE Journal of Solid-State Circuits. 53(11). 3163–3173. 87 indexed citations
14.
Gonugondla, Sujan K., et al.. (2018). Energy-Efficient Deep In-memory Architecture for NAND Flash Memories. 1–5. 8 indexed citations
15.
Kang, Mingu, Sujan K. Gonugondla, Ameya D. Patil, & Naresh R. Shanbhag. (2018). A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array. IEEE Journal of Solid-State Circuits. 53(2). 642–655. 160 indexed citations
16.
Kang, Mingu, Sujan K. Gonugondla, Sungmin Lim, et al.. (2018). PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms. 43–56. 39 indexed citations
17.
Kang, Mingu, Sujan K. Gonugondla, & Naresh R. Shanbhag. (2017). A 19.4 nJ/decision 364K decisions/s in-memory random forest classifier in 6T SRAM array. 263–266. 25 indexed citations
18.
Gonugondla, Sujan K., Byonghyo Shim, & Naresh R. Shanbhag. (2016). Perfect error compensation via algorithmic error cancellation. 966–970. 5 indexed citations
19.
Gonugondla, Sujan K., et al.. (2016). GDOT: A graphene-based nanofunction for dot-product computation. 1–2. 6 indexed citations
20.
Kang, Mingu, Sujan K. Gonugondla, Min-Sun Keel, & Naresh R. Shanbhag. (2015). An energy-efficient memory-based high-throughput VLSI architecture for convolutional networks. 1037–1041. 21 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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