88 citations · 255 across the 45 of their papers we have counts for
5 papers · 1 filter
Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation
Youngeun Kim, Joshua Chough, Priyadarshini Panda
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) because of their sparse, asynchronous, and binary event-drive…
Federated Learning with Spiking Neural Networks
Yeshwanth Venkatesha, Youngeun Kim, Leandros Tassiulas +1
As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emergin…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
PrivateSNN: Privacy-Preserving Spiking Neural Networks
Youngeun Kim, Yeshwanth Venkatesha, Priyadarshini Panda
How can we bring both privacy and energy-efficiency to a neural system? In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pr…
Visual Explanations from Spiking Neural Networks using Interspike Intervals
Youngeun Kim, Priyadarshini Panda
Spiking Neural Networks (SNNs) compute and communicate with asynchronous binary temporal events that can lead to significant energy savings with neuromorphic hardware. Recent algor…