18 citations · 27 across the 4 of their papers we have counts for
3 papers · 1 filter
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…