Guy Boudoukh

533 citations
3 papers · 296 indexed · 1 hit paper · h-index 3

Impact in

Papers in

Guy Boudoukh

3 papers receiving 286 citations

Hit Papers

Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? 2017 · 287 citations
2870+3+6Years since publication50100150200250

Peers

Guy Boudoukh
Comparison fields: 5 of 50
  • Hardware and Architecture 78
  • Computer Vision and Pattern Recognition 154
  • Computational Mathematics 4
  • Electrical and Electronic Engineering 161
  • Artificial Intelligence 87
Replace Shixuan Zheng with:
Shixuan Zheng China
Vikram Jain Belgium
Weinan Song United States
Madhura Purnaprajna India
Ahmad Shawahna Saudi Arabia
Zheng Qu United States
Jeff Pool United States
Andre Guntoro Germany
Jinwei Xu China
Guy Boudoukh relative to Shixuan Zheng China Shixuan Zheng's profile →
Citations per field
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Shixuan Zheng · 1×
Citations per year

Countries citing papers authored by Guy Boudoukh

Since Specialization
Citations

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

Fields of papers citing papers by Guy Boudoukh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Guy Boudoukh, 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 Guy Boudoukh Line = papers co-authored together Guy Boudoukh links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
Hit paper breakdown →
2017287
2 20096
3 20183

About Guy Boudoukh

Guy Boudoukh is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture, Electrical and Electronic Engineering, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 296 indexed citations. Recurring topics across this work include Embedded Systems Design Techniques (1 paper), Parallel Computing and Optimization Techniques (1 paper), Video Surveillance and Tracking Methods (1 paper), Image Enhancement Techniques (1 paper), Low-power high-performance VLSI design (1 paper) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Hardware and Architecture (78 citations), Computer Vision and Pattern Recognition (154 citations), Computational Mathematics (4 citations), Electrical and Electronic Engineering (161 citations) and Artificial Intelligence (87 citations). Guy Boudoukh has collaborated with scholars based in Israel and United States. Frequent co-authors include Debbie Marr, Ganesh Venkatesh, Suchit Subhaschandra, Randy Huang, Duncan J. M. Moss, Srivatsan Krishnan, Jaewoong Sim, Eriko Nurvitadhi, Ido Leichter and Ehud Rivlin. Their work appears in journals such as Zenodo (CERN European Organization for Nuclear Research).

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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