Michael Haselman

3.2k citations
15 papers · 954 indexed · 2 hit papers · h-index 10

Michael Haselman

15 papers receiving 931 citations

Hit Papers

A Configurable Cloud-Scale DNN Processor for Real-Time AI3632016202620192022100200300

Peers

Michael Haselman
Comparison fields: 5 of 58
  • Hardware and Architecture 396
  • Computer Networks and Communications 347
  • Computer Vision and Pattern Recognition 201
  • Radiation 85
  • Computational Mathematics 4
Replace Liang Sheng with:
Liang Sheng China
C. Johns United States
Massimo Torquati Italy
Kanad Basu United States
Salvatore Pontarelli Italy
A. Salsano Italy
Michael Day United States
J. Kahle United States
Lianmin Zheng China
Jieyang Chen United States
Michael Haselman relative to Liang Sheng China Liang Sheng's profile →
Citations per field
00.5×6.0×
Liang Sheng · 1×
Citations per year

Countries citing papers authored by Michael Haselman

Since Specialization
Citations

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

Fields of papers citing papers by Michael Haselman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Michael Haselman, 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 Michael Haselman Line = papers co-authored together Michael Haselman links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 20199
2
A Configurable Cloud-Scale DNN Processor for Real-Time AIbreakdown →
2018363
3 201721
4
A cloud-scale acceleration architecturebreakdown →
2016315
5 201226
6 20105
7 201017
8 200910
9 20096
10 200982
11 200919
12 20088
13 20084
14 200723
15 200546

About Michael Haselman

Michael Haselman is a scholar working on Hardware and Architecture, Radiation, Nuclear and High Energy Physics, Computer Networks and Communications and Radiology, Nuclear Medicine and Imaging, having authored 15 papers that have together received 954 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (6 papers), Particle Detector Development and Performance (5 papers), Medical Imaging Techniques and Applications (4 papers), Radiation Detection and Scintillator Technologies (4 papers), Advancements in PLL and VCO Technologies (2 papers), Interconnection Networks and 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 Hardware and Architecture (396 citations), Computer Networks and Communications (347 citations), Computer Vision and Pattern Recognition (201 citations), Radiation (85 citations) and Computational Mathematics (4 citations). Michael Haselman has collaborated with scholars based in United States, United Kingdom and Finland. Frequent co-authors include Scott Hauck, Michael Papamichael, Sitaram Lanka, Todd Massengill, Adrian M. Caulfield, Kalin Ovtcharov, Eric S. Chung, Doug Burger, Daniel Lo and Jeremy Fowers. Their work appears in journals such as IEEE Micro, Proceedings of the IEEE, IEEE Transactions on Nuclear Science and PubMed.

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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