Spiking neural networks (SNNs) are bioplausible machine learning models that use discrete
spikes to encode, compute, and transmit information. Combined with event-driven low …

Abstract Recurrent Spiking Neural Networks (RSNNs) are bio-plausible computational
models to detect temporal patterns in data and mimic nonlinear dynamical systems. Due to …

Neuromorphic computing describes the hardware implementation of biological neurons and
synapses of spiking neural networks (SNNs). We introduce SONIC, a software-defined …

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes
using a shared interconnect. We propose ADIONA, a dynamic segmented bus interconnect …

We present a CMOS-Memristor hybrid analog design of a neuromorphic crossbar array with
integrated inference and training. Each crosspoint on the crossbar includes a memristor to …

High-energy particles in space can induce single-and multibit upsets in random electronic
components of FPGA-based neuromorphic systems. We propose NeuFT, a low overhead …

Learning the underlying dynamics of periodic and aperiodic systems evolving over space
and time provides substantial challenges across scientific fields. Despite recent advances in …