13 citations · 14 across the 6 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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-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…