Suchit Subhaschandra
Impact in
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques
- Embedded Systems Design Techniques
Papers in
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- Parallel Computing and Optimization Techniques 7
- Embedded Systems Design Techniques 5
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- Low-power high-performance VLSI design 4
- Semiconductor materials and devices 1
- Photonic and Optical Devices 1
- Co-authors
- Eriko Nurvitadhi (5 shared papers)Debbie Marr (4 shared papers)Duncan J. M. Moss (4 shared papers)Jaewoong Sim (4 shared papers)Srivatsan Krishnan (4 shared papers)Ganesh Venkatesh (1 shared paper)Randy Huang (1 shared paper)Guy Boudoukh (1 shared paper)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
Suchit Subhaschandra
8 papers receiving 500 citations
Suchit Subhaschandra's Hit Papers
Peers
Comparison fields: 5 of 54
- Hardware and Architecture 213
- Computational Mathematics 7
- Computer Vision and Pattern Recognition 221
- Computer Networks and Communications 171
- Artificial Intelligence 143
Countries citing papers authored by Suchit Subhaschandra
This map shows the geographic impact of Suchit Subhaschandra'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 Suchit Subhaschandra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suchit Subhaschandra more than expected).
Fields of papers citing papers by Suchit Subhaschandra
This network shows the impact of papers produced by Suchit Subhaschandra. 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 Suchit Subhaschandra. The network helps show where Suchit Subhaschandra may publish in the future.
Co-authors
The 25 scholars most cited alongside Suchit Subhaschandra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? Hit paper breakdown → | 2017 | 287 |
| 2 | 2012 | 62 | |
| 3 | 2018 | 60 | |
| 4 | 2017 | 47 | |
| 5 | 2011 | 43 | |
| 6 | 2017 | 10 | |
| 7 | A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform A Deep Learning Case Study | 2018 | 7 |
| 8 | 2018 | 2 |
About Suchit Subhaschandra
Suchit Subhaschandra is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 8 papers that have together received 518 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (7 papers), Embedded Systems Design Techniques (5 papers), Low-power high-performance VLSI design (4 papers), Interconnection Networks and Systems (2 papers), Semiconductor materials and devices (1 paper), Numerical Methods and Algorithms (1 paper), Photonic and Optical Devices (1 paper) and Brain Tumor Detection and Classification (1 paper). The work is most often cited by research in Hardware and Architecture (213 citations), Computational Mathematics (7 citations), Computer Vision and Pattern Recognition (221 citations), Computer Networks and Communications (171 citations) and Artificial Intelligence (143 citations). Suchit Subhaschandra has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Eriko Nurvitadhi, Debbie Marr, Duncan J. M. Moss, Jaewoong Sim, Srivatsan Krishnan, Ganesh Venkatesh, Randy Huang, Guy Boudoukh, Philip H. W. Leong and Asit Mishra.
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.