Jun Sawada

6.5k total citations · 2 hit papers
9 papers, 4.2k citations indexed

About

Jun Sawada is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Biomedical Engineering. According to data from OpenAlex, Jun Sawada has authored 9 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 3 papers in Cognitive Neuroscience and 3 papers in Biomedical Engineering. Recurrent topics in Jun Sawada's work include Advanced Memory and Neural Computing (4 papers), Low-power high-performance VLSI design (3 papers) and Neural dynamics and brain function (3 papers). Jun Sawada is often cited by papers focused on Advanced Memory and Neural Computing (4 papers), Low-power high-performance VLSI design (3 papers) and Neural dynamics and brain function (3 papers). Jun Sawada collaborates with scholars based in United States and Japan. Jun Sawada's co-authors include John V. Arthur, Filipp Akopyan, Paul Merolla, Rodrigo Alvarez-Icaza, Bryan L. Jackson, Andrew S. Cassidy, Dharmendra S. Modha, W. P. Risk, Brian Taba and Yutaka Nakamura and has published in prestigious journals such as Science, IBM Journal of Research and Development and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Jun Sawada

9 papers receiving 4.0k citations

Hit Papers

A million spiking-neuron integrated circuit with a scalab... 2014 2026 2018 2022 2014 2015 500 1000 1.5k 2.0k 2.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jun Sawada United States 8 3.9k 1.4k 1.3k 1.3k 197 9 4.2k
Filipp Akopyan United States 8 4.2k 1.1× 1.6k 1.1× 1.5k 1.1× 1.4k 1.1× 199 1.0× 9 4.5k
Nabil Imam United States 10 4.0k 1.0× 1.5k 1.1× 1.5k 1.1× 1.4k 1.1× 199 1.0× 16 4.4k
Steven K. Esser United States 11 3.6k 0.9× 1.5k 1.1× 1.6k 1.2× 1.3k 1.0× 205 1.0× 12 4.4k
Rodrigo Alvarez-Icaza United States 8 4.6k 1.2× 1.7k 1.2× 1.8k 1.3× 1.6k 1.3× 211 1.1× 11 5.0k
Bernard Brezzo United States 9 3.9k 1.0× 1.4k 1.0× 1.3k 1.0× 1.3k 1.0× 201 1.0× 11 4.3k
Rathinakumar Appuswamy United States 8 3.3k 0.8× 1.2k 0.8× 1.1k 0.8× 1.2k 0.9× 192 1.0× 13 3.6k
Andrew S. Cassidy United States 18 4.8k 1.2× 1.7k 1.2× 1.8k 1.4× 1.7k 1.3× 207 1.1× 40 5.3k
Brian Taba United States 8 4.8k 1.2× 1.6k 1.1× 1.8k 1.4× 1.7k 1.3× 207 1.1× 8 5.3k
Ivan Vo United States 6 2.7k 0.7× 1.1k 0.8× 892 0.7× 854 0.7× 183 0.9× 12 2.9k
Paul Merolla United States 17 5.9k 1.5× 2.2k 1.6× 2.3k 1.8× 2.0k 1.6× 222 1.1× 24 6.4k

Countries citing papers authored by Jun Sawada

Since Specialization
Citations

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

Fields of papers citing papers by Jun Sawada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Sawada

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Sawada. A scholar is included among the top collaborators of Jun Sawada based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jun Sawada. Jun Sawada is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Cassidy, Andrew S., Jun Sawada, Paul Merolla, et al.. (2016). TrueNorth: A High-Performance, Low-Power Neurosynaptic Processor for Multi-Sensory Perception, Action, and Cognition. 12 indexed citations
2.
Akopyan, Filipp, Jun Sawada, Andrew S. Cassidy, et al.. (2015). TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 34(10). 1537–1557. 1064 indexed citations breakdown →
3.
Merolla, Paul, John V. Arthur, Rodrigo Alvarez-Icaza, et al.. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science. 345(6197). 668–673. 2780 indexed citations breakdown →
4.
Cassidy, Andrew S., Paul Merolla, John V. Arthur, et al.. (2013). Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. 1–10. 171 indexed citations
5.
Chang, Leland, Yutaka Nakamura, Robert K. Montoye, et al.. (2007). A 5.3GHz 8T-SRAM with Operation Down to 0.41V in 65nm CMOS. 252–253. 104 indexed citations
6.
Belluomini, Wendy, Damir A. Jamsek, Andrew Martin, et al.. (2006). Limited switch dynamic logic circuits for high-speed low-power circuit design. IBM Journal of Research and Development. 50(2.3). 277–286. 16 indexed citations
7.
Montoye, Robert K., et al.. (2006). Design of Shifting and Permutation Units using LSDL Circuit Family. 1692–1696. 1 indexed citations
8.
Belluomini, Wendy, Damir A. Jamsek, Andrew Martin, et al.. (2005). An 8Ghz floating-point multiply. 374–376. 15 indexed citations
9.
Montoye, Robert K., et al.. (2003). A double precision floating point multiply. 1. 336–337. 14 indexed citations

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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