22 citations · 96 across the 18 of their papers we have counts for
5 papers · 1 filter
TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Autonomous mobile agents require low-power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks while adapting to diverse environments, as mobile agen…
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Alberto Viale, Alberto Marchisio, Maurizio Martina +2
Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomo…
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks
Alessio Colucci, Andreas Steininger, Muhammad Shafique
Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs),…
tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher accuracy. However, employing such models on the resource- and energy-constrained embedde…
lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Recent advances have shown that SNN-based systems can efficiently perform unsupervised continual learning due to their bio-plausible learning rule, e.g., Spike-Timing-Dependent Pla…