3.2 A 1.95GHz fully integrated envelope elimination and restoration CMOS power amplifier with envelope/phase generator and timing aligner for WCDMA and LTE. - Kazuaki Oishi; Eiji Yoshida; Yasufumi Sakai; Hideki Takauchi; Yoichi Kawano; Noriaki Shirai; Hideki Kano; Masahiro Kudo; Tomotoshi Murakami; Tetsuro Tamura; Seitaro Kawai; Shinji Yamaura; et al. (ISSCC, 2014)

A 1.95 GHz Fully Integrated Envelope Elimination and Restoration CMOS Power Amplifier Using Timing Alignment Technique for WCDMA and LTE. - Kazuaki Oishi; Eiji Yoshida; Yasufumi Sakai; Hideki Takauchi; Yoichi Kawano; Noriaki Shirai; Hideki Kano; Masahiro Kudo; Tomotoshi Murakami; Tetsuro Tamura; Shigeaki Kawai; Kazuo Suto; et al. (IEEE J. Solid State Circuits, 2014)

3.5 A 56Gb/s NRZ-electrical 247mW/lane serial-link transceiver in 28nm CMOS. - Takayuki Shibasaki; Takumi Danjo; Yuuki Ogata; Yasufumi Sakai; Hiroki Miyaoka; Futoshi Terasawa; Masahiro Kudo; Hideki Kano; Atsushi Matsuda; Shigeaki Kawai; Tomoyuki Arai; Hirohito Higashi; et al. (ISSCC, 2016)

A 28.3 Gb/s 7.3 pJ/bit 35 dB backplane transceiver with eye sampling phase adaptation in 28 nm CMOS. - Hiroki Miyaoka; Futoshi Terasawa; Masahiro Kudo; Hideki Kano; Atsushi Matsuda; Noriaki Shirai; Shigeaki Kawai; Takayuki Shibasaki; Takumi Danjo; Yuuki Ogata; Yasufumi Sakai; Hisakatsu Yamaguchi; et al. (VLSI Circuits, 2016)

DropOut and DropConnect for Reliable Neuromorphic Inference under Energy and Bandwidth Constraints in Network Connectivity. - Yasufumi Sakai; Bruno U. Pedroni; Siddharth Joshi 001; Abraham Akinin; Gert Cauwenberghs (AICAS, 2019)

Neural-network assistance to calculate precise eigenvalue for fitness evaluation of real product design. - Yukito Tsunoda; Takahiro Notsu; Yasufumi Sakai; Naoki Hamada; Toshihiko Mori; Teruo Ishihara; Atsuki Inoue (GECCO (Companion), 2019)

Dropout and DropConnect for Reliable Neuromorphic Inference Under Communication Constraints in Network Connectivity. - Yasufumi Sakai; Bruno U. Pedroni; Siddharth Joshi 001; Satoshi Tanabe; Abraham Akinin; Gert Cauwenberghs (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

Greedy Search Algorithm for Mixed Precision in Post-Training Quantization of Convolutional Neural Network Inspired by Submodular Optimization. - Satoki Tsuji; Hiroshi Kawaguchi 001; Atsuki Inoue; Yasufumi Sakai; Fuyuka Yamada (ACML, 2021)

Efficient and Large Scale Pre-training Techniques for Japanese Natural Language Processing. - Akihiko Kasagi; Masahiro Asaoka; Akihiro Tabuchi; Yosuke Oyama; Takumi Honda; Yasufumi Sakai; Thang Dang; Tsuguchika Tabaru (CANDAR, 2021)

A High-Speed Neural Architecture Search Considering the Number of Weights. - Fuyuka Yamada; Satoki Tsuji; Hiroshi Kawaguchi 001; Atsuki Inoue; Yasufumi Sakai (KI, 2021)

Regularizing Data for Improving Execution Time of NLP Model. - Thang Dang; Yasufumi Sakai; Tsuguchika Tabaru; Akihiko Kasagi (FLAIRS, 2022)

Channel-wise quantization without accuracy degradation using Δloss analysis. - Kazuki Okado; Kengo Matsumoto; Atsuki Inoue; Hiroshi Kawaguchi 001; Yasufumi Sakai (ICMLT, 2022)

Automatic Pruning Rate Derivation for Structured Pruning of Deep Neural Networks. - Yasufumi Sakai; Akinori Iwakawa; Tsuguchika Tabaru; Atsuki Inoue; Hiroshi Kawaguchi 001 (ICPR, 2022)

Structured Pruning with Automatic Pruning Rate Derivation for Image Processing Neural Networks. - Yasufumi Sakai; Akinori Iwakawa; Tsuguchika Tabaru (ISMSI, 2022)

A Mixed-Precision Quantization Method without Accuracy Degradation Using Semilayers. - Kengo Matsumoto; Tomoya Matsuda; Atsuki Inoue; Hiroshi Kawaguchi 001; Yasufumi Sakai (ACML, 2023)

Semilayer-Wise Partial Quantization Without Accuracy Degradation or Back Propagation. - Tomoya Matsuda; Kengo Matsumoto; Atsuki Inoue; Hiroshi Kawaguchi 001; Yasufumi Sakai (ICANN (9), 2023)

Self-Supervised Learning with Atom Replacement for Catalyst Energy Prediction by Graph Neural Networks. - Yasufumi Sakai; Thang Dang; Shigeki Fukuta; Koichi Shirahata; Atsushi Ishikawa; Atsuki Inoue; Hiroshi Kawaguchi 001; Árni Björn Höskuldsson; Egill Skúlason (INNS DLIA@IJCNN, 2023)

Active Learning for Graph Neural Networks Training in Catalyst Energy Prediction. - Yasufumi Sakai; Naoki Matsumura; Atsuki Inoue; Hiroshi Kawaguchi 001; Thang Dang; Atsushi Ishikawa; Árni Björn Höskuldsson; Egill Skúlason (IJCNN, 2024)

S-DFP: shifted dynamic fixed point for quantized deep neural network training. - Yasufumi Sakai; Yutaka Tamiya (Neural Comput. Appl., 2025)

Greedy search algorithm for partial quantization of convolutional neural networks inspired by submodular optimization. - Satoki Tsuji; Fuyuka Yamada; Hiroshi Kawaguchi 001; Atsuki Inoue; Yasufumi Sakai (Neural Comput. Appl., 2025)

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models. - Yosuke Oyama; Yusuke Majima; Eiji Ohta; Yasufumi Sakai (CoRR, 2025)

Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations. - Naoki Matsumura; Yuta Yoshimoto; Yuto Iwasaki; Meguru Yamazaki; Yasufumi Sakai (CoRR, 2025)