Jaehoon Yu 研究室

主宰者：Jaehoon Yu

東京工業大学

AI 要約（直近 5 年の研究成果）

要約はまだ生成されていません。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（25 件）

[2025] <i>WhiteDwarf</i>: A Holistic Co-Design Approach to Ultra-Compact Neural Inference Acceleration
DOI: https://doi.org/10.1109/access.2025.3568916
[2025] Amorphica: A Fully Connected Annealer Supporting Metamorphic Annealing and Scalable Multi Chip Integration
DOI: https://doi.org/10.1109/access.2025.3620896
[2025] STARE: A Lightweight APT Detection Approach based on Graph Neural Network
DOI: https://doi.org/10.1109/iccc65529.2025.11149321
[2024] The Breakthrough Memory Solutions for Improved Performance on LLM Inference
DOI: https://doi.org/10.1109/mm.2024.3375352
[2024] Exploration of Hyperdimensional Computing Using Locality-Sensitive Hashing Mechanism on FPGA
DOI: https://doi.org/10.1109/icce59016.2024.10444323
[2024] Toward Improving Ensemble-Based Collaborative Inference at the Edge
DOI: https://doi.org/10.1109/access.2024.3351308
[2024] A similarity-based method for evaluating single-stroke regions
DOI: https://doi.org/10.1117/12.3049017
[2023] Pianissimo: A Sub-mW Class DNN Accelerator with Progressive Bit-by-Bit Datapath Architecture for Adaptive Inference at Edge
DOI: https://doi.org/10.23919/vlsitechnologyandcir57934.2023.10185293
[2023] TT-MLP: Tensor Train Decomposition on Deep MLPs
DOI: https://doi.org/10.1109/access.2023.3240784
[2023] Recurrent Residual Networks Contain Stronger Lottery Tickets
DOI: https://doi.org/10.1109/access.2023.3245808

続きを表示（残り 15 件）

[2023] Pianissimo: A Sub-mW Class DNN Accelerator With Progressively Adjustable Bit-Precision
DOI: https://doi.org/10.1109/access.2023.3347578
[2023] A Highly Accurate and Parallel Vision MLP FPGA Accelerator based on FP7/8 SIMD Operations
DOI: https://doi.org/10.1109/mcsoc60832.2023.00077
[2023] Decision Forest Training Accelerator Based on Binary Feature Decomposition
DOI: https://doi.org/10.1109/fccm57271.2023.00043
[2022] Hiddenite: CNN Inference Accelerator for Randomly Weighted Neural Networks
DOI: https://doi.org/10.7567/ssdm.2022.k-5-01
[2022] Hiddenite: 4K-PE Hidden Network Inference 4D-Tensor Engine Exploiting On-Chip Model Construction Achieving 34.8-to-16.0TOPS/W for CIFAR-100 and ImageNet
DOI: https://doi.org/10.1109/isscc42614.2022.9731668
[2022] Investigating Small Device Implementation of FRET-based Optical Reservoir Computing
DOI: https://doi.org/10.1109/mwscas54063.2022.9859432
[2022] Via-switch FPGA with transistor-free programmability enabling energy-efficient near-memory parallel computation
DOI: https://doi.org/10.35848/1347-4065/ac6b81
[2021] A High-Performance and Flexible FPGA Inference Accelerator for Decision Forests Based on Prior Feature Space Partitioning
DOI: https://doi.org/10.1109/icfpt52863.2021.9609699
[2021] Edge Inference Engine for Deep & Random Sparse Neural Networks with 4-bit Cartesian-Product MAC Array and Pipelined Activation Aligner
DOI: https://doi.org/10.1109/hcs52781.2021.9567328
[2021] A Study of Ensemble Learning for Randomly Weighted Neural Network
[2021] Clutter classification for cognitive radar with a deep convolutional neural network
DOI: https://doi.org/10.1049/icp.2021.0744
[2021] MUX Granularity Oriented Iterative Technology Mapping for Implementing Compute-Intensive Applications on Via-Switch FPGA
DOI: https://doi.org/10.23919/date51398.2021.9474202
[2021] ProgressiveNN: Achieving Computational Scalability with Dynamic Bit-Precision Adjustment by MSB-first Accumulative Computation
DOI: https://doi.org/10.15803/ijnc.11.2_338
[2021] ExtraFerns: Fully Parallel Ensemble Learning Technique with Random Projection and Non-Greedy yet Minimal Memory Access Training
DOI: https://doi.org/10.15803/ijnc.11.2_215
[2021] Hidden-Fold Networks: Random Recurrent Residuals Using Sparse Supermasks
DOI: https://doi.org/10.5244/c.35.371

科研費（0 件）

まだデータがありません（KAKEN 取り込み後に表示）。

所属学会・役職（0 件）

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AI 要約（直近 5 年の研究成果）

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関連研究室

研究成果（25 件）

科研費（0 件）

所属学会・役職（0 件）