Jeff Pool
Impact in
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
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- Advanced Neural Network Applications
- Advanced Vision and Imaging
Papers in
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- Parallel Computing and Optimization Techniques 6
- Embedded Systems Design Techniques 2
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- Low-power high-performance VLSI design 3
- Co-authors
- Anselmo Lastra (6 shared papers)Montek Singh (6 shared papers)Huizi Mao (2 shared papers)William J. Dally (2 shared papers)Song Han (2 shared papers)Wenshuo Li (1 shared paper)Yu Wang (1 shared paper)Xingyu Liu (1 shared paper)
- Journals
- Journal of Low Power Electronics (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (2 papers)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Jeff Pool
11 papers receiving 241 citations
Peers
Comparison fields: 5 of 41
- Hardware and Architecture 58
- Computer Vision and Pattern Recognition 161
- Computer Graphics and Computer-Aided Design 26
- Artificial Intelligence 82
- Signal Processing 24
Countries citing papers authored by Jeff Pool
This map shows the geographic impact of Jeff Pool'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 Jeff Pool with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Pool more than expected).
Fields of papers citing papers by Jeff Pool
This network shows the impact of papers produced by Jeff Pool. 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 Jeff Pool. The network helps show where Jeff Pool may publish in the future.
Co-authors
The 21 scholars most cited alongside Jeff Pool, 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 | 2017 | 142 | |
| 2 | 2008 | 27 | |
| 3 | 2010 | 20 | |
| 4 | 2011 | 19 | |
| 5 | DSD: Dense-Sparse-Dense Training for Deep Neural Networks | 2016 | 12 |
| 6 | Energy-precision tradeoffs in the graphics pipeline | 2012 | 10 |
| 7 | 2011 | 8 | |
| 8 | 2012 | 8 | |
| 9 | Channel Permutations for N:M Sparsity | 2021 | 6 |
| 10 | Self-Supervised Generative Adversarial Compression. | 2020 | 5 |
| 11 | 2024 | 1 |
About Jeff Pool
Jeff Pool is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering, Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 258 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (6 papers), Advanced Data Storage Technologies (3 papers), Low-power high-performance VLSI design (3 papers), Embedded Systems Design Techniques (2 papers), Advanced Neural Network Applications (2 papers), Numerical Methods and Algorithms (2 papers), Advanced Image Processing Techniques (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Hardware and Architecture (58 citations), Computer Vision and Pattern Recognition (161 citations), Computer Graphics and Computer-Aided Design (26 citations), Artificial Intelligence (82 citations) and Signal Processing (24 citations). Jeff Pool has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Anselmo Lastra, Montek Singh, Huizi Mao, William J. Dally, Song Han, Wenshuo Li, Yu Wang, Xingyu Liu, Chong Yu and Sharan Narang. Their work appears in journals such as Journal of Low Power Electronics, arXiv (Cornell University) and Neural Information Processing Systems.
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.