21 citations · 61 across the 19 of their papers we have counts for
5 papers · 1 filter
Techniques for Enhancing Memory Capacity of Reservoir Computing
Atsuki Yokota, Ichiro Kawashima, Yohei Saito +3
Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there i…
Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations
Ninnart Fuengfusin, Hakaru Tamukoh
Deploying deep neural networks (DNNs) in real-world environments poses challenges due to faults that can manifest in physical hardware from radiation, aging, and temperature fluctu…
Harden Deep Neural Networks Against Fault Injections Through Weight Scaling
Ninnart Fuengfusin, Hakaru Tamukoh
Deep neural networks (DNNs) have enabled smart applications on hardware devices. However, these hardware devices are vulnerable to unintended faults caused by aging, temperature va…
An Efficient Hardware-Oriented Dropout Algorithm
Yoeng Jye Yeoh, Takashi Morie, Hakaru Tamukoh
This paper proposes a hardware-oriented dropout algorithm, which is efficient for field programmable gate array (FPGA) implementation. In deep neural networks (DNNs), overfitting o…
Network with Sub-Networks
Ninnart Fuengfusin, Hakaru Tamukoh
We introduce network with sub-networks, a neural network which its weight layers could be detached into sub-neural networks during inference. To develop weights and biases which co…