The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …

Recent commercial hardware platforms for embedded real-time systems feature
heterogeneous processing units and computing accelerators on the same System-on-Chip …

The relentless proliferation of Big Data and artificial intelligence has compelled computing
platform architectures to evolve into heterogeneous multicores for greater energy efficiency …

This paper evaluates the combination of a Directed Acyclic Graph (DAG) task splitting
technique already proposed in the literature and the state-of-the-art, energy-aware version …

Nowadays, the proliferation of small satellites brings the skyrocketing rise in space data,
especially the shift to on-orbit computing needs. On one hand, with the increasing volume of …

Heterogeneous hardware platforms are often used for implementing complex critical real-
time applications, like Advanced driver-assistance systems (ADAS) and autonomous driving …

In order to satisfy timing constraints, modern real-time applications require massively
parallel accelerators such as General Purpose Graphic Processing Units (GPGPUs) …

The network-on-chip (NoC) provides a viable solution to bus-contention problems, that occur
in classical manycore architectures. However, NoC complex design requires a particular …

Many recent computing platforms combine CPUs with different types of accelerators such as
Graphical Processing Units (GPUs), to cope with the increasing computation needs of …

Shared resources on the multicore chip, such as main memory, are increasingly becoming a
point of contention. Traditional real-time task scheduling policies focus on solely on the …