Using accelerators based on analog computing is an efficient way to proc...
Binarized Neural Networks (BNNs) significantly reduce the computation an...
In many machine learning applications, e.g., tree-based ensembles, float...
To satisfy the increasing performance needs of modern cyber-physical sys...
In the near future, the development of autonomous driving will get more
...
In real-time systems, schedulability tests are utilized to provide timin...
Due to the growing popularity of the Internet of Things, edge computing
...
When considering recurrent tasks in real-time systems, concurrent access...
Neural networks (NNs) are known for their high predictive accuracy in co...
To reduce the resource demand of neural network (NN) inference systems, ...
Real-time systems increasingly use multicore processors in order to sati...
The performance of multiprocessor synchronization and locking protocols ...
Several emerging technologies for byte-addressable non-volatile memory (...
Non-volatile memory, such as resistive RAM (RRAM), is an emerging
energy...
During the execution of a job, it may suspend itself, i.e., its computat...
Many-core systems require inter-core communication, and network-on-chips...
In real-time systems, in addition to the functional correctness recurren...
Over the years, many multiprocessor locking protocols have been designed...
The sporadic task model is often used to analyze recurrent execution of
...
This paper considers the scheduling of parallel real-time tasks with
arb...
The period enforcer algorithm for self-suspending real-time tasks is a
t...
This report summarizes two general frameworks, namely k2Q and k2U, that ...
To deal with a large variety of workloads in different application domai...