Kodai Ueyoshi

633 total citations
22 papers, 455 citations indexed

About

Kodai Ueyoshi is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Kodai Ueyoshi has authored 22 papers receiving a total of 455 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 15 papers in Computer Vision and Pattern Recognition and 13 papers in Artificial Intelligence. Recurrent topics in Kodai Ueyoshi's work include Advanced Neural Network Applications (14 papers), Advanced Memory and Neural Computing (13 papers) and Machine Learning and ELM (7 papers). Kodai Ueyoshi is often cited by papers focused on Advanced Neural Network Applications (14 papers), Advanced Memory and Neural Computing (13 papers) and Machine Learning and ELM (7 papers). Kodai Ueyoshi collaborates with scholars based in Japan, Switzerland and Belgium. Kodai Ueyoshi's co-authors include Masato Motomura, Shinya Takamaeda-Yamazaki, Kota Ando, Tadahiro Kuroda, Tetsuya Asai, Masayuki Ikebe, Mototsugu Hamada, Hiroki Nakahara, Haruyoshi Yonekawa and Shimpei Sato and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems I Regular Papers and IEEE Transactions on Circuits & Systems II Express Briefs.

In The Last Decade

Kodai Ueyoshi

20 papers receiving 449 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kodai Ueyoshi Japan 9 373 185 115 63 37 22 455
Roel Uytterhoeven Belgium 5 320 0.9× 201 1.1× 99 0.9× 80 1.3× 29 0.8× 7 407
Haruyoshi Yonekawa Japan 9 292 0.8× 238 1.3× 113 1.0× 41 0.7× 20 0.5× 11 408
Daisuke Miyashita Japan 12 400 1.1× 112 0.6× 132 1.1× 33 0.5× 46 1.2× 35 501
Jinwook Oh South Korea 13 324 0.9× 239 1.3× 95 0.8× 69 1.1× 66 1.8× 34 510
En-Yu Yang United States 8 494 1.3× 131 0.7× 151 1.3× 91 1.4× 58 1.6× 15 620
Abinash Mohanty United States 11 521 1.4× 330 1.8× 202 1.8× 104 1.7× 40 1.1× 18 711
Jaehyeong Sim South Korea 10 241 0.6× 194 1.0× 113 1.0× 41 0.7× 27 0.7× 22 337
Shengyuan Zhou China 8 213 0.6× 173 0.9× 122 1.1× 95 1.5× 51 1.4× 14 378
Daniel Bankman United States 10 361 1.0× 102 0.6× 121 1.1× 33 0.5× 22 0.6× 13 437
Alfio Di Mauro Italy 12 333 0.9× 73 0.4× 79 0.7× 111 1.8× 67 1.8× 33 449

Countries citing papers authored by Kodai Ueyoshi

Since Specialization
Citations

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

Fields of papers citing papers by Kodai Ueyoshi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kodai Ueyoshi

This figure shows the co-authorship network connecting the top 25 collaborators of Kodai Ueyoshi. A scholar is included among the top collaborators of Kodai Ueyoshi 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 Kodai Ueyoshi. Kodai Ueyoshi 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.
Ueyoshi, Kodai, Ioannis A. Papistas, Vikram Jain, et al.. (2022). DIANA: An End-to-End Energy-Efficient Digital and ANAlog Hybrid Neural Network SoC. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 1–3. 49 indexed citations
2.
Jain, Vikram, Kodai Ueyoshi, Ioannis A. Papistas, et al.. (2022). DIANA: An End-to-End Hybrid DIgital and ANAlog Neural Network SoC for the Edge. IEEE Journal of Solid-State Circuits. 58(1). 203–215. 39 indexed citations
3.
Shiba, K., Kodai Ueyoshi, Kota Ando, et al.. (2020). A 3D-Stacked SRAM using Inductive Coupling with Low-Voltage Transmitter and 12:1 SerDes. 1–5. 3 indexed citations
4.
Shiba, K., Kodai Ueyoshi, Shinya Takamaeda-Yamazaki, et al.. (2020). A 96-MB 3D-Stacked SRAM Using Inductive Coupling With 0.4-V Transmitter, Termination Scheme and 12:1 SerDes in 40-nm CMOS. IEEE Transactions on Circuits and Systems I Regular Papers. 68(2). 692–703. 19 indexed citations
5.
Ando, Kota, Kodai Ueyoshi, Takumi Kudo, et al.. (2019). Dither NN: Hardware/Algorithm Co-Design for Accurate Quantized Neural Networks. IEICE Transactions on Information and Systems. E102.D(12). 2341–2353. 1 indexed citations
6.
Ando, Kota, Kodai Ueyoshi, Masayuki Ikebe, et al.. (2018). Quantization error-based regularization for hardware-aware neural network training. Nonlinear Theory and Its Applications IEICE. 9(4). 453–465.
7.
Ando, Kota, Kodai Ueyoshi, Takumi Kudo, et al.. (2018). Dither NN: An Accurate Neural Network with Dithering for Low Bit-Precision Hardware. abs 1511 363. 6–13. 6 indexed citations
8.
Ueyoshi, Kodai, Kota Ando, Shinya Takamaeda-Yamazaki, et al.. (2018). QUEST: A 7.49TOPS multi-purpose log-quantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS. 71 indexed citations
9.
Ueyoshi, Kodai, Takao Marukame, Tetsuya Asai, Masato Motomura, & Alexandre Schmid. (2017). Live demonstration: Feature extraction system using restricted Boltzmann machines on FPGA. 63. 1–1. 1 indexed citations
10.
Ando, Kota, Kodai Ueyoshi, Masayuki Ikebe, et al.. (2017). Logarithmic Compression for Memory Footprint Reduction in Neural Network Training. abs 1212 5701. 291–297. 1 indexed citations
11.
Yonekawa, Haruyoshi, Shimpei Sato, Hiroki Nakahara, et al.. (2017). In-memory area-efficient signal streaming processor design for binary neural networks. 345. 116–119. 6 indexed citations
12.
Ando, Kota, Kodai Ueyoshi, Haruyoshi Yonekawa, et al.. (2017). BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W. IEEE Journal of Solid-State Circuits. 53(4). 983–994. 115 indexed citations
13.
14.
Ueyoshi, Kodai, et al.. (2017). Exploring optimized accelerator design for binarized convolutional neural networks. 2510–2516. 6 indexed citations
15.
Takamaeda-Yamazaki, Shinya, Kodai Ueyoshi, Kota Ando, et al.. (2017). Accelerating deep learning by binarized hardware. abs 1602 2830. 1045–1051. 3 indexed citations
16.
17.
Marukame, Takao, Kodai Ueyoshi, Tetsuya Asai, et al.. (2016). Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation. IEEE Transactions on Circuits & Systems II Express Briefs. 64(4). 462–466. 13 indexed citations
18.
Ueyoshi, Kodai, Takao Marukame, Tetsuya Asai, Masato Motomura, & Alexandre Schmid. (2016). FPGA Implementation of a Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines. Circuits and Systems. 7(9). 2132–2141. 11 indexed citations
19.
Ueyoshi, Kodai, Takao Marukame, Tetsuya Asai, Masato Motomura, & Alexandre Schmid. (2016). Robustness of hardware-oriented restricted Boltzmann machines in deep belief networks for reliable processing. Nonlinear Theory and Its Applications IEICE. 7(3). 395–406. 7 indexed citations
20.
Ueyoshi, Kodai, Takao Marukame, Tetsuya Asai, Masato Motomura, & Alexandre Schmid. (2016). Memory-error tolerance of scalable and highly parallel architecture for restricted Boltzmann machines in Deep Belief Network. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 7. 357–360. 5 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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