Daniel Lo

1.5k citations
13 papers · 811 · 2 hit papers · h-index 7

Impact in

Papers in

Daniel Lo

13 papers receiving 792 citations

Daniel Lo's Hit Papers

A Configurable Cloud-Scale DNN Processor for Real-Time AI 2018 · 363 citations
3630+3+6Years since publication100200300

Peers

Daniel Lo
Comparison fields: 5 of 47
  • Hardware and Architecture 426
  • Computer Networks and Communications 378
  • Computer Vision and Pattern Recognition 197
  • Artificial Intelligence 228
  • Information Systems 153
Replace Stephen Heil with:
Stephen Heil United Kingdom
Todd Massengill United Kingdom
Lifeng Nai United States
Sitaram Lanka United States
Tae Jun Ham South Korea
Yuze Chi United States
Omer Khan United States
Jae W. Lee South Korea
Emmanuel Amaro United States
Mehrzad Samadi United States
Daniel Lo relative to Stephen Heil United Kingdom Stephen Heil's profile →
Citations per field
00.5×1.5×
Stephen Heil · 1×
Citations per year

Countries citing papers authored by Daniel Lo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Lo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

13 of 13 papers shown
#Work
1
A Configurable Cloud-Scale DNN Processor for Real-Time AI
Hit paper breakdown →
2018363
2
A cloud-scale acceleration architecture
Hit paper breakdown →
2016315
3 201052
4 201721
5 201519
6 20149
7 20199
8 20176
9 20155
10 20165
11 20123
12 20233
13 20241

About Daniel Lo

Daniel Lo is a scholar working on Hardware and Architecture, Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 811 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (10 papers), Embedded Systems Design Techniques (4 papers), Interconnection Networks and Systems (4 papers), Real-Time Systems Scheduling (3 papers), Cloud Computing and Resource Management (2 papers), Radiation Effects in Electronics (2 papers), Green IT and Sustainability (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Hardware and Architecture (426 citations), Computer Networks and Communications (378 citations), Computer Vision and Pattern Recognition (197 citations), Artificial Intelligence (228 citations) and Information Systems (153 citations). Daniel Lo has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Eric S. Chung, Michael Papamichael, Doug Burger, Stephen Heil, Todd Massengill, Jeremy Fowers, Adrian M. Caulfield, Michael Haselman, Kalin Ovtcharov and Sitaram Lanka. Their work appears in journals such as IEEE Micro, Proceedings of the ACM on Programming Languages and eCommons (Cornell University).

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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