continuous-time dynamics 1differentiable training 1memory efficiency 1neuromorphic computing 1spiking neural networks 1
From the 1 of 2 linked papers with an AI index.
2 papers
cs.LG2026
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi +1
The paper proposes a memory‑efficient training method for continuous‑time spiking neural networks by discretizing spike times into differentiable weighted events, enabling deep SNN…
cs.LG2025
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…