Ganesh Venkatesh
- Hardware and Architecture top 0.5%
- Electrical and Electronic Engineering top 5%
- Computer Networks and Communications top 2%
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Co-authors
- Jack SampsonSteven SwansonMichael TaylorDebbie MarrEriko NurvitadhiSaturnino GarciaJose Lugo-MartinezJaewoong Sim
- Topics
- Parallel Computing and Optimization Techniques (15 papers)Low-power high-performance VLSI design (6 papers)Interconnection Networks and Systems (6 papers)
- Partner nations
- United StatesIsraelCzechia
In The Last Decade
Ganesh Venkatesh
22 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Hardware and Architecture 834
- Electrical and Electronic Engineering 664
- Computer Networks and Communications 645
- Computer Vision and Pattern Recognition 422
- Artificial Intelligence 290
Countries citing papers authored by Ganesh Venkatesh
This map shows the geographic impact of Ganesh Venkatesh'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 Ganesh Venkatesh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ganesh Venkatesh more than expected).
Fields of papers citing papers by Ganesh Venkatesh
This network shows the impact of papers produced by Ganesh Venkatesh. 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 Ganesh Venkatesh. The network helps show where Ganesh Venkatesh may publish in the future.
Co-authorship network of co-authors of Ganesh Venkatesh
This figure shows the co-authorship network connecting the top 25 collaborators of Ganesh Venkatesh. A scholar is included among the top collaborators of Ganesh Venkatesh 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 Ganesh Venkatesh. Ganesh Venkatesh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 3 | |
| 3 | 29 | |
| 4 | 1 | |
| 5 | Mixed Precision Training | 62 |
| 6 | Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?breakdown → | 287 |
| 7 | 17 | |
| 8 | 76 | |
| 9 | 223 | |
| 10 | 12 | |
| 11 | 2 | |
| 12 | Quasi-ASICs: Trading Area for Energy by Exploiting Similarity in Synthesized Cores for Irregular Code | 0 |
| 13 | 99 | |
| 14 | 3 | |
| 15 | 1 | |
| 16 | 249 | |
| 17 | 48 | |
| 18 | 81 | |
| 19 | 2 | |
| 20 | 9 |
About Ganesh Venkatesh
Ganesh Venkatesh is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 24 papers that have together received 1.5k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (15 papers), Low-power high-performance VLSI design (6 papers) and Interconnection Networks and Systems (6 papers). The work is most often cited by research in Hardware and Architecture (834 citations), Computer Networks and Communications (645 citations) and Computational Mathematics (15 citations). Ganesh Venkatesh has collaborated with scholars based in United States, Israel and Czechia. Frequent co-authors include Jack Sampson, Steven Swanson, Michael Taylor, Debbie Marr, Eriko Nurvitadhi, Saturnino Garcia, Jose Lugo-Martinez, Jaewoong Sim, Asit Mishra and David Sheffield. Their work appears in journals such as IEEE Access, ACM SIGPLAN Notices and ACM SIGOPS Operating Systems Review.
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.