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