1 citations · 1 across the 4 of their papers we have counts for
4 papers
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi +1
Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware. However, training…
Harnessing Nonidealities in Analog In-Memory Computing Circuits: A Physical Modeling Approach for Neuromorphic Systems
Yusuke Sakemi, Yuji Okamoto, Takashi Morie +3
Large-scale deep learning models are increasingly constrained by their immense energy consumption, limiting their scalability and applicability for edge intelligence. In-memory com…
Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding
Yusuke Sakemi, Kakei Yamamoto, Takeo Hosomi +1
The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training scheme…
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks
Yusuke Sakemi, Takashi Morie, Takeo Hosomi +1
The spiking neural network (SNN) has been attracting considerable attention not only as a mathematical model for the brain, but also as an energy-efficient information processing m…