5 citations · 5 across the 1 of their papers we have counts for
3 papers
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…