Prasanth Chatarasi

735 citations
14 papers · 444 · h-index 8

Impact in

Papers in

Prasanth Chatarasi

13 papers receiving 436 citations

Peers

Prasanth Chatarasi
Comparison fields: 5 of 42
  • Computational Mathematics 20
  • Hardware and Architecture 220
  • Computer Vision and Pattern Recognition 195
  • Computer Networks and Communications 119
  • Electrical and Electronic Engineering 224
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Citations per field
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Citations per year

Countries citing papers authored by Prasanth Chatarasi

Since Specialization
Citations

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

Fields of papers citing papers by Prasanth Chatarasi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Prasanth Chatarasi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Prasanth Chatarasi Line = papers co-authored together Prasanth Chatarasi links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 2019209
2 2020124
3 202123
4 202123
5 201517
6 202414
7 202110
8 20199
9
A Data-Centric Approach for Modeling and Estimating Efficiency of Dataflows for Accelerator Design
20185
10
Understanding Reuse, Performance, and Hardware Cost of DNN Dataflows: A Data-Centric Approach
20183
11
Polyhedral Transformations of Explicitly Parallel Programs
20153
12 20183
13
MARVEL: A Decoupled Model-driven Approach for Efficiently Mapping Convolutions on Spatial DNN Accelerators.
20201
14 20150

About Prasanth Chatarasi

Prasanth Chatarasi is a scholar working on Hardware and Architecture, Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 14 papers that have together received 444 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (12 papers), Advanced Data Storage Technologies (4 papers), Embedded Systems Design Techniques (3 papers), Distributed and Parallel Computing Systems (3 papers), Advanced Neural Network Applications (3 papers), Logic, programming, and type systems (2 papers), Cloud Computing and Resource Management (2 papers) and Advanced Memory and Neural Computing (2 papers). The work is most often cited by research in Computational Mathematics (20 citations), Hardware and Architecture (220 citations), Computer Vision and Pattern Recognition (195 citations), Computer Networks and Communications (119 citations) and Electrical and Electronic Engineering (224 citations). Prasanth Chatarasi has collaborated with scholars based in United States and South Korea. Frequent co-authors include Vivek Sarkar, Tushar Krishna, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, Jun Shirako, Sivasankaran Rajamanickam, Jeffrey Young, Jason Riedy and Thomas M. Conte. Their work appears in journals such as IEEE Micro, IEEE Transactions on Parallel and Distributed Systems, ACM Transactions on Architecture and Code Optimization, OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) and arXiv (Cornell University).

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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