Dan Hammerstrom

1.6k citations
47 papers · 1.2k · h-index 15

Impact in

Papers in

Dan Hammerstrom

44 papers receiving 1.1k citations

Peers

Dan Hammerstrom
Comparison fields: 5 of 108
  • Hardware and Architecture 194
  • Artificial Intelligence 517
  • Electrical and Electronic Engineering 554
  • Computer Networks and Communications 210
  • Signal Processing 74
Replace Karim Mohammadi with:
Karim Mohammadi Iran
Chengjun Wu China
Xin Fu United States
Tajana Rosing United States
Hiroaki Kobayashi Japan
Sheng Lin China
Talal Bonny United Arab Emirates
Tarek El‐Ghazawi United States
Haoyu Yang Hong Kong
Steven R. Young United States
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Citations per field
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Citations per year

Countries citing papers authored by Dan Hammerstrom

Since Specialization
Citations

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

Fields of papers citing papers by Dan Hammerstrom

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1993184
2 1993182
3 1990161
4 2015116
5 200769
6 197754
7 199142
8 200741
9 197737
10 199637
11 198232
12 200828
13 201626
14 199021
15
Digital VLSI for neural networks
199819
16 201012
17 200912
18 200212
19
The connectivity analysis of simple association –or– how many connections do you need
198811
20 200310

About Dan Hammerstrom

Dan Hammerstrom is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture, Computer Networks and Communications and Cognitive Neuroscience, having authored 47 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (17 papers), Advanced Memory and Neural Computing (16 papers), CCD and CMOS Imaging Sensors (9 papers), Neural dynamics and brain function (6 papers), Parallel Computing and Optimization Techniques (6 papers), Ferroelectric and Negative Capacitance Devices (5 papers), Neural Networks and Reservoir Computing (5 papers) and Analog and Mixed-Signal Circuit Design (4 papers). The work is most often cited by research in Hardware and Architecture (194 citations), Artificial Intelligence (517 citations), Electrical and Electronic Engineering (554 citations), Computer Networks and Communications (210 citations) and Signal Processing (74 citations). Dan Hammerstrom has collaborated with scholars based in United States, India and Japan. Frequent co-authors include Edward S. Davidson, George I. Bourianoff, Wolfgang Porod, Ian A. Young, Tadashi Shibata, Dmitri E. Nikonov, György Csaba, R. Iris Bahar, Diana Marculescu and Massoud Pedram. Their work appears in journals such as Neural Networks, IEEE Transactions on Nanotechnology, Artificial Intelligence Review, ACM SIGPLAN Notices and Computer.

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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