13 citations · 13 across the 3 of their papers we have counts for
3 papers
Skip2-LoRA: A Lightweight On-device DNN Fine-tuning Method for Low-cost Edge Devices
Hiroki Matsutani, Masaaki Kondo, Kazuki Sunaga +1
This paper proposes Skip2-LoRA as a lightweight fine-tuning method for deep neural networks to address the gap between pre-trained and deployed models. In our approach, trainable L…
An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithm
Kazuki Sunaga, Keisuke Sugiura, Hiroki Matsutani
A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector. node2vec is a well-known algorithm to obtain such a graph…
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…