Jun Deguchi

714 total citations
41 papers, 545 citations indexed

About

Jun Deguchi is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Jun Deguchi has authored 41 papers receiving a total of 545 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Electrical and Electronic Engineering, 10 papers in Computer Vision and Pattern Recognition and 9 papers in Artificial Intelligence. Recurrent topics in Jun Deguchi's work include Advanced Memory and Neural Computing (11 papers), CCD and CMOS Imaging Sensors (9 papers) and Advancements in PLL and VCO Technologies (9 papers). Jun Deguchi is often cited by papers focused on Advanced Memory and Neural Computing (11 papers), CCD and CMOS Imaging Sensors (9 papers) and Advancements in PLL and VCO Technologies (9 papers). Jun Deguchi collaborates with scholars based in Japan, United States and Belgium. Jun Deguchi's co-authors include Daisuke Miyashita, Shouhei Kousai, Tomoya Suzuki, Radu Berdan, Yoshifumi Nishi, Takao Marukame, Shosuke Fujii, Kensuke Ota, Masumi Saitoh and M. Yamaguchi and has published in prestigious journals such as Journal of Applied Physics, IEEE Journal of Solid-State Circuits and Japanese Journal of Applied Physics.

In The Last Decade

Jun Deguchi

35 papers receiving 517 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Deguchi Japan 12 477 97 96 80 70 41 545
Martino Dazzi Switzerland 8 414 0.9× 112 1.2× 65 0.7× 64 0.8× 24 0.3× 12 446
Soonwan Kwon South Korea 7 495 1.0× 114 1.2× 63 0.7× 57 0.7× 38 0.5× 12 570
Kohji Hosokawa Japan 8 422 0.9× 92 0.9× 52 0.5× 72 0.9× 25 0.4× 29 442
Seungchul Jung South Korea 12 671 1.4× 107 1.1× 72 0.8× 67 0.8× 133 1.9× 37 747
Sungmeen Myung South Korea 6 463 1.0× 106 1.1× 54 0.6× 49 0.6× 37 0.5× 9 526
Ming Cheng China 13 467 1.0× 124 1.3× 38 0.4× 89 1.1× 27 0.4× 27 571
Wooseok Yi South Korea 8 429 0.9× 141 1.5× 54 0.6× 47 0.6× 24 0.3× 16 542
Chrong-Jung Lin Taiwan 10 538 1.1× 44 0.5× 85 0.9× 74 0.9× 37 0.5× 33 581
Seung Keun Yoon South Korea 6 401 0.8× 98 1.0× 57 0.6× 51 0.6× 59 0.8× 11 494
Siyan Lin China 7 348 0.7× 57 0.6× 73 0.8× 120 1.5× 33 0.5× 19 432

Countries citing papers authored by Jun Deguchi

Since Specialization
Citations

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

Fields of papers citing papers by Jun Deguchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Deguchi

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Deguchi. A scholar is included among the top collaborators of Jun Deguchi 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 Deguchi. Jun Deguchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tanamoto, Tetsufumi, et al.. (2024). Noise properties in the Coulomb blockade region of FinFETs. Japanese Journal of Applied Physics. 63(3). 03SP69–03SP69.
2.
Nakata, Kengo, Daisuke Miyashita, Jun Deguchi, & Ryuichi Fujimoto. (2024). Accelerating CNN Inference with an Adaptive Quantization Method Using Computational Complexity-Aware Regularization. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. E108.A(2). 149–159.
3.
Kobayashi, Hiroyuki, Yutaka Shimizu, Yuji Satoh, et al.. (2022). A 25.6-Gb/s Interface Employing PAM-4-Based Four-Channel Multiplexing and Cascaded Clock and Data Recovery Circuits in Ring Topology for High-Bandwidth and Large-Capacity Storage Systems. IEEE Journal of Solid-State Circuits. 57(5). 1517–1526. 2 indexed citations
4.
Berdan, Radu, Takao Marukame, Kensuke Ota, et al.. (2020). Low-power linear computation using nonlinear ferroelectric tunnel junction memristors. Nature Electronics. 3(5). 259–266. 183 indexed citations
5.
Ota, Kensuke, et al.. (2020). Performance Maximization of In-Memory Reinforcement Learning with Variability-Controlled Hf1-xZrxO2 Ferroelectric Tunnel Junctions -- *. IEICE Technical Report; IEICE Tech. Rep.. 119(397). 9–9. 1 indexed citations
6.
Kobayashi, Hiroyuki, et al.. (2020). A Noise-Canceling Charge Pump for Area Efficient PLL Design. 31–33. 2 indexed citations
7.
Miyashita, Daisuke, Shin‐ichi Sasaki, Kengo Nakata, et al.. (2020). Weight Compression MAC Accelerator for Effective Inference of Deep Learning. IEICE Transactions on Electronics. E103.C(10). 514–523. 4 indexed citations
8.
Miyashita, Daisuke, et al.. (2019). A 12.8-Gb/s Daisy Chain-Based Downlink I/F Employing Spectrally Compressed Multi-Band Multiplexing for High-Bandwidth, Large-Capacity Storage Systems. IEEE Journal of Solid-State Circuits. 54(4). 1086–1095. 4 indexed citations
9.
Deguchi, Jun, et al.. (2019). Can in-memory/analog accelerators be a silver bullet for energy-efficient inference?. 22.4.1–22.4.4. 3 indexed citations
10.
Tanamoto, Tetsufumi, Yoshifumi Nishi, & Jun Deguchi. (2019). Quantum Annealing Machines Based on Semiconductor Nanostructures. Journal of the Physical Society of Japan. 88(6). 61013–61013. 2 indexed citations
11.
Berdan, Radu, Takao Marukame, Kensuke Ota, et al.. (2019). In-memory Reinforcement Learning with Moderately-Stochastic Conductance Switching of Ferroelectric Tunnel Junctions. T22–T23. 30 indexed citations
12.
Miyashita, Daisuke, et al.. (2018). FPGA-based CNN Processor with Filter-Wise-Optimized Bit Precision. 47–50. 11 indexed citations
13.
Miyashita, Daisuke, Shouhei Kousai, Tomoya Suzuki, & Jun Deguchi. (2016). Time-domain neural network: A 48.5 TSOp/s/W neuromorphic chip optimized for deep learning and CMOS technology. 25–28. 18 indexed citations
14.
15.
Miyashita, Daisuke, Hiroyuki Kobayashi, Jun Deguchi, Shouhei Kousai, & Mototsugu Hamada. (2011). A −104dBc/Hz in-band phase noise 3GHz all digital PLL with phase interpolation based hierarchical time to digital convertor. 112–113. 13 indexed citations
16.
17.
Sugimura, Takeaki, et al.. (2006). Low-Power and High-Sensitivity Magnetoresistive Random Access Memory Sensing Scheme with Body-Biased Preamplifier. Japanese Journal of Applied Physics. 45(4S). 3321–3321. 3 indexed citations
18.
Sugimura, Takeaki, et al.. (2005). Robot Vision System with Three-Dimensionally Integrated Reconfigurable Image Processor Chip. IEICE technical report. Speech. 105(43). 79–84. 1 indexed citations
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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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