Tetsuya Asai 研究室

主宰者：Tetsuya Asai

北海道大学

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。

外部リンク

研究成果（59 件）

[2026] Decentralized learning in dynamic edge systems via expertise collaboration and partial model integration
DOI: https://doi.org/10.1587/nolta.17.875
[2025] Acquisition of physical kinetics in reciprocating machines using reservoir computing for anomaly detection
DOI: https://doi.org/10.1587/nolta.16.848
[2025] Efficient semi-supervised learning via multi-augmentations on single samples for edge AI training
DOI: https://doi.org/10.1587/nolta.16.817
[2025] Prompt engineering for generating high-quality conversations and its evaluation
DOI: https://doi.org/10.1587/nolta.16.909
[2025] Design and evaluation of analog predictive coding networks based on the free-energy principle
DOI: https://doi.org/10.1587/nolta.16.271
[2025] Variable-parallelism reconfigurable architecture for efficient and flexible CNN acceleration
DOI: https://doi.org/10.1587/nolta.16.422
[2025] BBBNN: Bernoulli binary Bayesian neural network accelerator toward edge explainable AI inference
DOI: https://doi.org/10.1587/nolta.16.788
[2025] ATLAS: Adaptive Tuning of Layer Sharing for Multi-Task Federated Learning
DOI: https://doi.org/10.2299/jsp.29.87
[2025] Seq2seq Architecture with Memory-Centric Neural Network Processor towards Edge AI Computing
DOI: https://doi.org/10.2299/jsp.29.83
[2025] Split weight distribution learning for binary neural networks exploiting nano-memristive graphene/sumanene/graphene devices
DOI: https://doi.org/10.35848/1347-4065/adbbfb

続きを表示（残り 49 件）

[2025] Multi-Level Split Weight Learning for Multi-Cell ReRAM Computation-in-Memory
DOI: https://doi.org/10.23919/snw65111.2025.11097270
[2024] Edge AI Online Training Architecture Using Multi-Phase-Quantization Optimizer
DOI: https://doi.org/10.1109/ijcnn60899.2024.10650299
[2024] Common Bases Hypothesis: exploring multi-task collaborative learning of neural networks
DOI: https://doi.org/10.1587/nolta.16.79
[2024] Proposal and evaluation of recurrent neural network training by multi-phase quantization optimizer
DOI: https://doi.org/10.1587/nolta.16.30
[2024] Resource-efficient streaming architecture for ensemble Kalman filters designed for online learning in physical reservoir computing
DOI: https://doi.org/10.1587/nolta.16.120
[2024] Evaluation of the encoder-decoder model's common representation acquisition toward its application in edge computing
DOI: https://doi.org/10.1587/nolta.16.132
[2024] Multi-phase-quantization optimizer and its architecture for edge AI training
DOI: https://doi.org/10.1587/nolta.16.43
[2024] Extending Binary Neural Networks to Bayesian Neural Networks with Probabilistic Interpretation of Binary Weights
DOI: https://doi.org/10.1587/transinf.2023lop0009
[2024] Stochastic In-Memory Computing and its Applications to Artificial Intelligence
DOI: https://doi.org/10.1587/essfr.18.1_71
[2024] Out-of-distribution Data Detection using Bayesian Convolutional Neural Network with Variational Inference
DOI: https://doi.org/10.1109/ijcnn60899.2024.10650576
[2024] Highly-integrable analogue reservoir circuits based on a simple cycle architecture
DOI: https://doi.org/10.1038/s41598-024-61880-z
[2024] SPCTRE: sparsity-constrained fully-digital reservoir computing architecture on FPGA
DOI: https://doi.org/10.1080/17445760.2024.2310576
[2024] Stochastic memory device based on a bistable system model with a simple analog circuit
DOI: https://doi.org/10.1587/nolta.15.249
[2024] Acquisition of physical kinetics of permanent magnet dc motor by reservoir computing
DOI: https://doi.org/10.1587/nolta.15.899
[2024] High-order polynomial activation function and regenerative internal weights for FPGA implementation of reservoir computing
DOI: https://doi.org/10.1587/nolta.15.262
[2024] Physical reservoir replication using small-scale digital calibration reservoir
DOI: https://doi.org/10.1587/nolta.15.851
[2024] Performance evaluation of Bayesian neural networks in detecting out-of-distribution image data and a study on data preprocessing
DOI: https://doi.org/10.1587/nolta.15.709
[2023] On the Control of Computing-in-memory Devices with Resource-efficient Digital Circuits towards their On-chip Learning
DOI: https://doi.org/10.1587/nolta.14.639
[2023] Agile Manufacturing and Education in the DX Era
DOI: https://doi.org/10.5104/jiep.26.p3
[2023] Design and evaluation of brain-inspired predictive coding networks based on the free-energy principle for novel neuromorphic hardware
DOI: https://doi.org/10.1587/nolta.15.107
[2023] Cellar automata models for reservoir computing in single-walled carbon nanotube network complexed with polyoxometalate
DOI: https://doi.org/10.1587/nolta.15.17
[2023] A novel small‐signal detection method using divergence properties of second‐order linear differential equations
DOI: https://doi.org/10.1049/ell2.12928
[2023] Fabrication and Training of 3D Conductive Polymer Networks for Neuromorphic Wetware
DOI: https://doi.org/10.1002/adfm.202300903
[2023] Active charge balancer for CMOS integration of an array of neural stimulators
DOI: https://doi.org/10.1587/nolta.14.319
[2022] Physical Implementation of Reservoir Computing through Electrochemical Reaction (Adv. Sci. 6/2022)
DOI: https://doi.org/10.1002/advs.202270036
[2022] Noise sensitivity of physical reservoir computing in a ring array of atomic switches
DOI: https://doi.org/10.1587/nolta.13.373
[2022] An information theoretic parameter tuning for MEMS-based reservoir computing
DOI: https://doi.org/10.1587/nolta.13.459
[2022] Behavioral Model of Molecular Gap-Type Atomic Switches and Its SPICE Integration
DOI: https://doi.org/10.4236/cs.2022.131001
[2022] Digital implementation of a multilayer perceptron based on stochastic computing with online learning function
DOI: https://doi.org/10.1587/nolta.13.324
[2022] Heuristic model for configurable polymer wire synaptic devices
DOI: https://doi.org/10.1587/nolta.13.379
[2022] Smart hardware architecture with random weight elimination and weight balancing algorithms
DOI: https://doi.org/10.1587/nolta.13.336
[2022] Holmes: A Hardware-Oriented Optimizer Using Logarithms
DOI: https://doi.org/10.1587/transinf.2022pap0001
[2022] A 1-Msps 500-Node FORCE Learning Accelerator for Reservoir Computing
DOI: https://doi.org/10.2299/jsp.26.103
[2021] Improvement of Controllability of a Nonlinear MEMS Resonator Type Reservoir based on Mutual Information Criteria
[2021] Neuromorphic Devices using Spatial Free Wiring of Conductive Polymer for Hardware Artificial Neural Networks
[2021] Real-Time Tone Mapping: A Survey and Cross-Implementation Hardware Benchmark
DOI: https://doi.org/10.1109/tcsvt.2021.3060143
[2021] Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses
DOI: https://doi.org/10.3390/polym13020312
[2021] Hardware-oriented deep reinforcement learning for edge computing
DOI: https://doi.org/10.1587/nolta.12.526
[2021] Creation of polymer wire synapse capable of forming long-term and short-term memory
[2021] Physical Reservoir Computing on Atomic Switch Arrays with High Precision and Excellent Memory Characteristics
[2021] [Keynote Lecture] On the neuromorphic materials, devices and systems towards super smart society
[2021] Simple Reservoir Computing Capitalizing on the Nonlinear Response of Materials: Theory and Physical Implementations
DOI: https://doi.org/10.1103/physrevapplied.15.024030
[2021] Physical Implementation of Reservoir Computing through Electrochemical Reaction
DOI: https://doi.org/10.1002/advs.202104076
[2021] Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate
DOI: https://doi.org/10.1088/2634-4386/ac4339
[2021] Applying a Molecular Device to Stochastic Computing Operation for a Hardware AI System Design
DOI: https://doi.org/10.2299/jsp.25.221
[2021] Resistance switching properties of conductive polymer wire synapses
[2021] Neuromorphic Information Processing and its Integrated Systems
[2021] Reservoir Computing on Atomic Switch Arrays with High Precision and Excellent Memory Characteristics
DOI: https://doi.org/10.2299/jsp.25.123
[2021] Construction of Nonlinear Large-Scale Network Simulator aiming for Emergence of Reservoir function

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）

まだデータがありません（学会データ連携後に表示）。

AI 要約（直近 5 年の研究成果）

外部リンク

関連研究室(8 件)

研究成果（59 件）

科研費（0 件）

所属学会・役職（0 件）