Sudarshan Srinivasan

1.6k total citations · 1 hit paper
35 papers, 911 citations indexed

About

Sudarshan Srinivasan is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Computer Networks and Communications. According to data from OpenAlex, Sudarshan Srinivasan has authored 35 papers receiving a total of 911 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Hardware and Architecture, 16 papers in Electrical and Electronic Engineering and 15 papers in Computer Networks and Communications. Recurrent topics in Sudarshan Srinivasan's work include Parallel Computing and Optimization Techniques (17 papers), Radiation Effects in Electronics (8 papers) and Low-power high-performance VLSI design (7 papers). Sudarshan Srinivasan is often cited by papers focused on Parallel Computing and Optimization Techniques (17 papers), Radiation Effects in Electronics (8 papers) and Low-power high-performance VLSI design (7 papers). Sudarshan Srinivasan collaborates with scholars based in United States, India and Brazil. Sudarshan Srinivasan's co-authors include Srikanth Kandula, Yuanyuan Zhou, Tushar Krishna, Hyoukjun Kwon, Eric Qin, Bharat Kaul, Ananda Samajdar, Dipankar Das, Zhenmin Li and Yuanyuan Zhou and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Sudarshan Srinivasan

32 papers receiving 872 citations

Hit Papers

SIGMA: A Sparse and Irreg... 2020 2026 2022 2024 2020 50 100 150 200 250
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. SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training
    2020 288 citations Eric Qin, Hyoukjun Kwon et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sudarshan Srinivasan United States 11 489 356 297 243 164 35 911
Nachiket Kapre Singapore 19 576 1.2× 732 2.1× 427 1.4× 176 0.7× 144 0.9× 87 1.1k
Joseph L. Greathouse United States 15 680 1.4× 808 2.3× 351 1.2× 142 0.6× 122 0.7× 34 1.0k
Björn Franke United Kingdom 17 638 1.3× 940 2.6× 136 0.5× 259 1.1× 113 0.7× 82 1.2k
Frank Hannig Germany 16 532 1.1× 807 2.3× 333 1.1× 95 0.4× 212 1.3× 162 1.2k
Daniel Lo United States 7 378 0.8× 426 1.2× 314 1.1× 228 0.9× 197 1.2× 13 811
Nathan DeBardeleben United States 17 683 1.4× 545 1.5× 729 2.5× 263 1.1× 74 0.5× 71 1.2k
Yuwei Hu United States 10 213 0.4× 342 1.0× 231 0.8× 341 1.4× 463 2.8× 12 858
Mehrzad Samadi United States 14 482 1.0× 725 2.0× 437 1.5× 119 0.5× 116 0.7× 30 944
Gregory Diamos United States 20 823 1.7× 800 2.2× 107 0.4× 300 1.2× 228 1.4× 29 1.3k

Countries citing papers authored by Sudarshan Srinivasan

Since Specialization
Citations

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

Fields of papers citing papers by Sudarshan Srinivasan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sudarshan Srinivasan

This figure shows the co-authorship network connecting the top 25 collaborators of Sudarshan Srinivasan. A scholar is included among the top collaborators of Sudarshan Srinivasan 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 Sudarshan Srinivasan. Sudarshan Srinivasan 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.
Srinivasan, Sudarshan, et al.. (2025). FRED: A Wafer-scale Fabric for 3D Parallel DNN Training. 34–48.
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Srinivasan, Sudarshan, et al.. (2022). Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients. PLoS ONE. 17(1). e0262182–e0262182. 21 indexed citations
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Qin, Eric, Sheng-Chun Kao, Hyoukjun Kwon, et al.. (2021). Extending Sparse Tensor Accelerators to Support Multiple Compression Formats. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1014–1024. 10 indexed citations
6.
Srinivasan, Sudarshan, et al.. (2021). A Lightweight Error-Resiliency Mechanism for Deep Neural Networks. 311–316. 10 indexed citations
7.
Sridharan, Srinivas, et al.. (2020). ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms. 81–92. 34 indexed citations
8.
Srinivasan, Sudarshan, et al.. (2020). Reliability Evaluation of Compressed Deep Learning Models. 1–5. 20 indexed citations
9.
Kalamkar, Dhiraj, Kunal Banerjee, Sudarshan Srinivasan, et al.. (2019). Training Google Neural Machine Translation on an Intel CPU Cluster. abs 1810 4805. 1–10. 4 indexed citations
10.
Srinivasan, Sudarshan, Israel Koren, & Sandip Kundu. (2015). Online mechanism for reliability and power-efficiency management of a dynamically reconfigurable core. 4. 327–334. 4 indexed citations
11.
Srinivasan, Sudarshan, et al.. (2013). A study on polymorphing superscalar processor dynamically to improve power efficiency. 46–51. 1 indexed citations
12.
Srinivasan, Sudarshan, Raghavan Kumar, & Sandip Kundu. (2013). Program phase duration prediction and its application to fine-grain power management. 127–132. 7 indexed citations
13.
Srinivasan, Sudarshan, et al.. (2013). On dynamic polymorphing of a superscalar core for improving energy efficiency. 37. 495–498.
14.
Srinivasan, Sudarshan, et al.. (2013). Split-Path Fused Floating Point Multiply Accumulate (FPMAC). 17–24. 9 indexed citations
15.
Srinivasan, Sudarshan, et al.. (2012). A Wavelet-Based Spatio-Temporal Heat Dissipation Model for Reordering of Program Phases to Produce Temperature Extremes in a Chip. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 31(12). 1867–1880. 5 indexed citations
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Koduru, Srinivas V., Ranjeet Ranjan Kumar, Sudarshan Srinivasan, & Chendil Damodaran. (2009). 1078 Inactivation of Notch signaling by Withaferin-A in human colon cancer. European Journal of Cancer Supplements. 7(2). 108–108. 2 indexed citations
18.
Srinivasan, Sudarshan, et al.. (2004). Flashback: a lightweight extension for rollback and deterministic replay for software debugging. USENIX Annual Technical Conference. 22(2). 3–3. 223 indexed citations
19.
Uttamchandani, Sandeep, Kaladhar Voruganti, Sudarshan Srinivasan, John D. Palmer, & David Pease. (2004). Polus: Growing Storage QoS Management Beyond a 4-Year Old Kid. File and Storage Technologies. 31–44. 23 indexed citations
20.
Li, Zhenmin, Zhifeng Chen, Sudarshan Srinivasan, & Yuanyuan Zhou. (2004). C-Miner: Mining Block Correlations in Storage Systems. File and Storage Technologies. 13–13. 121 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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