9 papers
FeNN-DMA: A RISC-V SoC for SNN acceleration
Zainab Aizaz, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks…
Space as Time Through Neuron Position Learning
Balázs Mészáros, James C. Knight, Danyal Akarca +1
Biological neural networks exist in physical space where distance influences communication delays: a fundamental coupling between space and time absent in most artificial neural ne…
A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks
James C. Knight, Johanna Senk, Thomas Nowotny
The majority of research in both training Artificial Neural Networks (ANNs) and modeling learning in biological brains focuses on synaptic plasticity, where learning equates to cha…
Constructive community race: full-density spiking neural network model drives neuromorphic computing
Johanna Senk, Anno C. Kurth, Steve Furber +18
The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a mo…
A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2
Balázs Mészáros, James C. Knight, Jonathan Timcheck +1
Spiking Neural Networks are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks for edge computing. Neuromorphic computi…
Efficient Event-based Delay Learning in Spiking Neural Networks
Balázs Mészáros, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Net…