Dedicated accelerator hardware has become essential for processing AI-based workloads,
leading to the rise of novel accelerator architectures. Furthermore, fundamental differences …

Recent works have introduced task-based parallelization schemes to accelerate graph
search and sparse data-structure traversal, where some solutions scale up to thousands of …

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms
of energy consumption and latency when performing inference with deep learning …

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms
of energy consumption and latency when performing inference with deep learning …

Recent years have been characterized by increasing interest in graph computations. This
trend can be related to the large number of potential application areas. Moreover, increasing …

Parallel graph algorithms have become one of the principal applications of high-
performance computing besides numerical simulations and machine learning workloads …

AI hardware technologies have revolutionized computational science. While they have been
mostly used to accelerate deep learning training and inference models for machine learning …

Graph search and sparse data-structure traversal workloads contain challenging irregular
memory patterns on global data structures that need to be modified atomically. Distributed …

Artificial neural networks have been used for a multitude of regression tasks, and their
descendants have expanded the domain to many applications such as image and speech …

A new trend in processor architecture design is the packaging of thousands of small
processor cores into a single device, where there is no device-level shared memory but …