We examine the challenging “marriage” between computational efficiency and biological
plausibility—A crucial node in the domain of spiking neural networks at the intersection of …

Spiking Neural Networks are a class of Artificial Neural Networks that closely mimic
biological neural networks. They are particularly interesting because of their potential to …

Despite the successes of graphics processing units (GPUs) in accelerating simulations in
several research fields, their use is largely restricted to domain-specific workloads that …

Spiking Neural Networks (SNNs) are a class of Artificial Neural Networks that show a time
behaviour that cannot be computed with single one-shot functions. Therefore, to study their …

Modern high-performance, large-scale simulations require significant computational power,
memory, and storage, making heterogeneous architectures an attractive option. The …

In the era of ubiquitous Artificial Intelligence and power-hungry Neural Networks, the brain
offers prime inspiration for a faster, greener, more efficient, and arguably more effective …

Agent-Based Simulation has shown ability. However, ABS suffers from long execution times.
Using heterogeneous hardware is a solution. However, significant effort is required to …

From the reliability in a wireless sensor network to the formation of traffic jams,
spatiotemporal patterns are key in understanding how complex behaviors can emerge in a …

Modern high-performance, large-scale simulations require significant computational power,
memory, and storage, making heterogeneous architectures an attractive option. The …