3 papers
cs.LG2024
Magic for the Age of Quantized DNNs
Yoshihide Sawada, Ryuji Saiin, Kazuma Suetake
Recently, the number of parameters in DNNs has explosively increased, as exemplified by LLMs (Large Language Models), making inference on small-scale computers more difficult. Mode…
cs.NE2023
Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks
Ryuji Saiin, Tomoya Shirakawa, Sota Yoshihara +2
In this article, we propose a new paradigm for training spiking neural networks (SNNs), spike accumulation forwarding (SAF). It is known that SNNs are energy-efficient but difficul…
cs.NE2022
Rethinking the role of normalization and residual blocks for spiking neural networks
Shin-ichi Ikegawa, Ryuji Saiin, Yoshihide Sawada +1
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive fir…