Shin-ichi Ikegawa

most citedS

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NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

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…

cs.LG2022★ 1 cited

S $^{3}$ NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks

Kazuma Suetake, Shin-ichi Ikegawa, Ryuji Saiin +1

As the scales of neural networks increase, techniques that enable them to run with low computational cost and energy efficiency are required. From such demands, various efficient n…

Rethinking the role of normalization and residual blocks for spiking neural networks

S3NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks

S $^{3}$ NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks