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.
SCNN
2017584 citationsAngshuman Parashar, Minsoo Rhu et al.profile →
Timeloop: A Systematic Approach to DNN Accelerator Evaluation
2019344 citationsAngshuman Parashar, Priyanka Raina et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Angshuman Parashar
Since
Specialization
Citations
This map shows the geographic impact of Angshuman Parashar'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 Angshuman Parashar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Angshuman Parashar more than expected).
Fields of papers citing papers by Angshuman Parashar
This network shows the impact of papers produced by Angshuman Parashar. 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 Angshuman Parashar. The network helps show where Angshuman Parashar may publish in the future.
Co-authorship network of co-authors of Angshuman Parashar
This figure shows the co-authorship network connecting the top 25 collaborators of Angshuman Parashar.
A scholar is included among the top collaborators of Angshuman Parashar 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 Angshuman Parashar. Angshuman Parashar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kao, Sheng-Chun, Hyoukjun Kwon, Michael Pellauer, Angshuman Parashar, & Tushar Krishna. (2022). A Formalism of DNN Accelerator Flexibility. ACM SIGMETRICS Performance Evaluation Review. 50(1). 53–54.1 indexed citations
4.
Kao, Sheng-Chun, Hyoukjun Kwon, Michael Pellauer, Angshuman Parashar, & Tushar Krishna. (2022). A Formalism of DNN Accelerator Flexibility. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 6(2). 1–23.3 indexed citations
Parashar, Angshuman, Priyanka Raina, Yakun Sophia Shao, et al.. (2019). Timeloop: A Systematic Approach to DNN Accelerator Evaluation. 304–315.344 indexed citations breakdown →
11.
Kwon, Hyoukjun, Prasanth Chatarasi, Michael Pellauer, et al.. (2018). A Data-Centric Approach for Modeling and Estimating Efficiency of Dataflows for Accelerator Design. arXiv (Cornell University).5 indexed citations
12.
Kwon, Hyoukjun, Prasanth Chatarasi, Michael Pellauer, et al.. (2018). Understanding Reuse, Performance, and Hardware Cost of DNN Dataflows: A Data-Centric Approach. arXiv (Cornell University).3 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.