1 citations · 2 across the 4 of their papers we have counts for
4 papers
TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy, Charlotte Frenkel
The demand for low-power inference and training of deep neural networks (DNNs) on edge devices has intensified the need for algorithms that are both scalable and energy-efficient.…
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks
Prajna G. Malettira, Shubham Negi, Wachirawit Ponghiran +1
Spiking Neural Networks (SNNs) with their bio-inspired Leaky Integrate-and-Fire (LIF) neurons inherently capture temporal information. This makes them well-suited for sequential ta…
LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Marco Paul E. Apolinario, Arani Roy, Kaushik Roy
Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption, particularly for on-dev…
Event-based Temporally Dense Optical Flow Estimation with Sequential Learning
Wachirawit Ponghiran, Chamika Mihiranga Liyanagedera, Kaushik Roy
Event cameras provide an advantage over traditional frame-based cameras when capturing fast-moving objects without a motion blur. They achieve this by recording changes in light in…