17 citations · 26 across the 8 of their papers we have counts for
4 papers · 1 filter
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.…
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…
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy
Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly…
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks
Marco Paul E. Apolinario, Adarsh Kumar Kosta, Utkarsh Saxena +1
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge de…