Designing neuromorphic sensory processing units (NSPUs) based on Temporal Neural Networks (TNNs) is a highly challenging task due to the reliance on manual, labor-intensive hardware development processes. TNNs have been identified as highly promising for real-time edge AI applications, mainly because they are energy-efficient and bio-inspired. However, available methodologies lack automation and are not very… →

Artificial Life (ALife) research explores the emergence of lifelike behaviors through computational simulations, providing a unique framework to study “life as it could be.” However, the field faces significant limitations: a reliance on manually crafted simulation rules and configurations. This process is time-intensive and constrained by human intuition, leaving many potential discoveries unexplored. Researchers often… →

Deploying Deep Neural Networks (DNNs) on edge devices, such as smartphones and autonomous vehicles, remains a significant challenge due to their computationally intensive nature.  Most existing pruning algorithms struggle to balance high compression rates and inference accuracy and have to be compatible with commercial hardware—unstructured pruning yields irregular sparsity that often limits its usage in… →

A direct correlation exists between an LLM’s training corpus quality and its capabilities. Consequently, researchers have invested a great deal of effort into curating extensive, high-quality datasets, which, at present, are achievable with craftful human annotations. Man-made datasets, however, have one downside: their reliance becomes increasingly unsustainable as complexity grows.  Many methods have been worked… →