6 papers
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…
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…
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…
FeNN: A RISC-V vector processor for Spiking Neural Network acceleration
Zainab Aizaz, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) have the potential to drastically reduce the energy requirements of AI systems. However, mainstream accelerators like GPUs and TPUs are designed for…
Eventprop training for efficient neuromorphic applications
Thomas Shoesmith, James C. Knight, Balázs Mészáros +2
Neuromorphic computing can reduce the energy requirements of neural networks and holds the promise to `repatriate' AI workloads back from the cloud to the edge. However, training n…
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…