An Overflow/Underflow-Free Fixed-Point Bit-Width Optimization Method for OS-ELM Digital Circuit

no code implementations • 17 Mar 2021 • 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 data without data transfers to remote servers.

An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning

no code implementations • 10 May 2020 • Hirohisa Watanabe, Mineto Tsukada, Hiroki Matsutani

In addition, we propose a combination of L2 regularization and spectral normalization for the on-device reinforcement learning so that output values of the neural network can be fit into a certain range and the reinforcement learning becomes stable.

L2 Regularization OpenAI Gym +3

An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices

no code implementations • 27 Feb 2020 • Rei Ito, Mineto Tsukada, Hiroki Matsutani

We extend it for an on-device federated learning so that edge devices can exchange their trained results and update their model by using those collected from the other edge devices.

Anomaly Detection Federated Learning

A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices

no code implementations • 23 Jul 2019 • Mineto Tsukada, Masaaki Kondo, Hiroki Matsutani

However, (1) the iterative optimization often requires significant efforts to follow changes in the distribution of normal data (i. e., concept drift), and (2) data transfers between edge and server impose additional latency and energy consumption.

Semi-supervised Anomaly Detection Supervised Anomaly Detection +1