Haruyoshi Yonekawa

547 total citations
11 papers, 408 citations indexed

About

Haruyoshi Yonekawa is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Haruyoshi Yonekawa has authored 11 papers receiving a total of 408 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 8 papers in Electrical and Electronic Engineering and 4 papers in Artificial Intelligence. Recurrent topics in Haruyoshi Yonekawa's work include Advanced Neural Network Applications (8 papers), Advanced Memory and Neural Computing (4 papers) and Advanced Image and Video Retrieval Techniques (4 papers). Haruyoshi Yonekawa is often cited by papers focused on Advanced Neural Network Applications (8 papers), Advanced Memory and Neural Computing (4 papers) and Advanced Image and Video Retrieval Techniques (4 papers). Haruyoshi Yonekawa collaborates with scholars based in Japan. Haruyoshi Yonekawa's co-authors include Hiroki Nakahara, Shimpei Sato, Masato Motomura, Kota Ando, Shinya Takamaeda-Yamazaki, Masayuki Ikebe, Tetsuya Asai, Tadahiro Kuroda, Kodai Ueyoshi and Tetsuo Fujii and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEICE Transactions on Information and Systems and IEICE Technical Report; IEICE Tech. Rep..

In The Last Decade

Haruyoshi Yonekawa

11 papers receiving 400 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Haruyoshi Yonekawa Japan	9	292	238	113	41	22	11	408
Ahmad Shawahna Saudi Arabia	4	166 0.6×	155 0.7×	127 1.1×	60 1.5×	11 0.5×	5	354
Jonghoon Jin United States	6	226 0.8×	286 1.2×	111 1.0×	56 1.4×	7 0.3×	10	384
Zhengang Li United States	13	225 0.8×	238 1.0×	181 1.6×	55 1.3×	12 0.5×	41	512
Matthieu Courbariaux France	3	167 0.6×	311 1.3×	219 1.9×	38 0.9×	9 0.4×	4	436
Tuan Nghia Nguyen South Korea	3	193 0.7×	230 1.0×	77 0.7×	28 0.7×	43 2.0×	10	360
Shixuan Zheng China	7	275 0.9×	167 0.7×	146 1.3×	83 2.0×	8 0.4×	8	415
Xitian Fan China	9	208 0.7×	238 1.0×	90 0.8×	70 1.7×	17 0.8×	13	375
Maurizio Capra Italy	5	172 0.6×	116 0.5×	108 1.0×	45 1.1×	12 0.5×	7	365
Taesik Na United States	12	283 1.0×	147 0.6×	109 1.0×	34 0.8×	6 0.3×	29	403

Countries citing papers authored by Haruyoshi Yonekawa

Since Specialization

Citations

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

Fields of papers citing papers by Haruyoshi Yonekawa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haruyoshi Yonekawa

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

All Works

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11 of 11 papers shown

Nakahara, Hiroki, et al.. (2019). GUINNESS: A GUI Based Binarized Deep Neural Network Framework for Software Programmers. IEICE Transactions on Information and Systems. E102.D(5). 1003–1011. 13 indexed citations

Yonekawa, Haruyoshi, Shimpei Sato, & Hiroki Nakahara. (2018). A Ternary Weight Binary Input Convolutional Neural Network: Realization on the Embedded Processor. 174–179. 11 indexed citations

Nakahara, Hiroki, et al.. (2018). A Lightweight YOLOv2. 31–40. 99 indexed citations

Nakahara, Hiroki, et al.. (2017). GUINNESS: A GUI based Binarized Deep Neural Network Framework for an FPGA. IEICE Technical Report; IEICE Tech. Rep.. 117(221). 51–56. 2 indexed citations

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

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

Nakahara, Hiroki, et al.. (2017). A demonstration of the GUINNESS: A GUI based neural NEtwork SyntheSizer for an FPGA. 1–1. 9 indexed citations

Nakahara, Hiroki, et al.. (2017). A Batch Normalization Free Binarized Convolutional Deep Neural Network on an FPGA (Abstract Only). 290–290. 9 indexed citations

Ando, Kota, Kodai Ueyoshi, Haruyoshi Yonekawa, et al.. (2017). BRein memory: A 13-layer 4.2 K neuron/0.8 M synapse binary/ternary reconfigurable in-memory deep neural network accelerator in 65 nm CMOS. 57 indexed citations

10.

Nakahara, Hiroki, Haruyoshi Yonekawa, & Shimpei Sato. (2017). An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA. 168–175. 16 indexed citations

11.

Yonekawa, Haruyoshi & Hiroki Nakahara. (2017). On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA. 98–105. 71 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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