19 citations · 67 across the 15 of their papers we have counts for
16 papers
Performance Improvement of Federated Learning Server using Smart NIC
Naoki Shibahara, Michihiro Koibuchi, Hiroki Matsutani
Federated learning is a distributed machine learning approach where local weight parameters trained by clients locally are aggregated as global parameters by a server. The global p…
An Integrated FPGA Accelerator for Deep Learning-based 2D/3D Path Planning
Keisuke Sugiura, Hiroki Matsutani
Path planning is a crucial component for realizing the autonomy of mobile robots. However, due to limited computational resources on mobile robots, it remains challenging to deploy…
A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices
Takeya Yamada, Hiroki Matsutani
A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phen…
Federated Learning of Neural ODE Models with Different Iteration Counts
Yuto Hoshino, Hiroki Kawakami, Hiroki Matsutani
Federated learning is a distributed machine learning approach in which clients train models locally with their own data and upload them to a server so that their trained results ar…
An Efficient Accelerator for Deep Learning-based Point Cloud Registration on FPGAs
Keisuke Sugiura, Hiroki Matsutani
Point cloud registration is the basis for many robotic applications such as odometry and Simultaneous Localization And Mapping (SLAM), which are increasingly important for autonomo…
Addressing Gap between Training Data and Deployed Environment by On-Device Learning
Kazuki Sunaga, Masaaki Kondo, Hiroki Matsutani
The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article intr…
Accelerating Distributed Deep Reinforcement Learning by In-Network Experience Sampling
Masaki Furukawa, Hiroki Matsutani
A computing cluster that interconnects multiple compute nodes is used to accelerate distributed reinforcement learning based on DQN (Deep Q-Network). In distributed reinforcement l…
A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs
Hiroki Kawakami, Hirohisa Watanabe, Keisuke Sugiura +1
High-performance deep neural network (DNN)-based systems are in high demand in edge environments. Due to its high computational complexity, it is challenging to deploy DNNs on edge…
An Overflow/Underflow-Free Fixed-Point Bit-Width Optimization Method for OS-ELM Digital Circuit
Mineto Tsukada, Hiroki Matsutani
Currently there has been increasing demand for real-time training on resource-limited IoT devices such as smart sensors, which realizes standalone online adaptation for streaming d…
A Universal LiDAR SLAM Accelerator System on Low-cost FPGA
Keisuke Sugiura, Hiroki Matsutani
LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) serves as a basis for indoor cleaning, navigation, and many other useful applications in both indus…
Accelerating ODE-Based Neural Networks on Low-Cost FPGAs
Hirohisa Watanabe, Hiroki Matsutani
ODENet is a deep neural network architecture in which a stacking structure of ResNet is implemented with an ordinary differential equation (ODE) solver. It can reduce the number of…
An FPGA Acceleration and Optimization Techniques for 2D LiDAR SLAM Algorithm
Keisuke Sugiura, Hiroki Matsutani
An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In…
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
Hirohisa Watanabe, Mineto Tsukada, Hiroki Matsutani
DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNs require a large buffer and batch processing for an experience rep…
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices
Rei Ito, Mineto Tsukada, Hiroki Matsutani
Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines. However, retraining or customizing a model is required at e…
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices
Mineto Tsukada, Masaaki Kondo, Hiroki Matsutani
Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches…
LaKe: An Energy Efficient, Low Latency, Accelerated Key-Value Store
Yuta Tokusashi, Hiroki Matsutani, Noa Zilberman
Key-value store is a popular type of cloud computing applications. The performance of key-value store applications have been shown to be very sensitive to load within the data cent…