Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Günzel, Mario"'
Autor:
Lin, Ching-Chi, Günzel, Mario, Shi, Junjie, Seidl, Tristan Taylan, Chen, Kuan-Hsun, Chen, Jian-Jia
Ensuring timing guarantees for every individual tasks is critical in real-time systems. Even for periodic tasks, providing timing guarantees for tasks with segmented self-suspending behavior is challenging due to timing anomalies, i.e., the reduction
Externí odkaz:
http://arxiv.org/abs/2409.09061
The Robot Operating System 2 (ROS~2) is a widely used middleware that provides software libraries and tools for developing robotic systems. In these systems, tasks are scheduled by ROS~2 executors. Since the scheduling behavior of the default ROS~2 e
Externí odkaz:
http://arxiv.org/abs/2408.03696
Occasional deadline misses are acceptable for soft real-time systems. Quantifying probabilistic and deterministic characteristics of deadline misses is therefore essential to ensure that deadline misses indeed happen only occasionally. This is suppor
Externí odkaz:
http://arxiv.org/abs/2401.15503
To satisfy the increasing performance needs of modern cyber-physical systems, multiprocessor architectures are increasingly utilized. To efficiently exploit their potential parallelism in hard real-time systems, appropriate task models and scheduling
Externí odkaz:
http://arxiv.org/abs/2208.11830
In real-time systems, schedulability tests are utilized to provide timing guarantees. However, for self-suspending task sets, current suspension-aware schedulability tests are limited to Task-Level Fixed-Priority~(TFP) scheduling or Earliest-Deadline
Externí odkaz:
http://arxiv.org/abs/2111.09725
Neural networks (NNs) are known for their high predictive accuracy in complex learning problems. Beside practical advantages, NNs also indicate favourable theoretical properties such as universal approximation (UA) theorems. Binarized Neural Networks
Externí odkaz:
http://arxiv.org/abs/2102.02631
Autor:
Buschjäger, Sebastian, Chen, Jian-Jia, Chen, Kuan-Hsun, Günzel, Mario, Morik, Katharina, Novkin, Rodion, Pfahler, Lukas, Yayla, Mikail
To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tuned trading accuracy with energy consumption and performance. Tuning t
Externí odkaz:
http://arxiv.org/abs/2102.01344
Real-time systems increasingly use multicore processors in order to satisfy thermal, power, and computational requirements. To exploit the architectural parallelism offered by the multicore processors, parallel task models, scheduling algorithms and
Externí odkaz:
http://arxiv.org/abs/2101.11053
Autor:
Buschjäger, Sebastian, Chen, Jian-Jia, Chen, Kuan-Hsun, Günzel, Mario, Hakert, Christian, Morik, Katharina, Novkin, Rodion, Pfahler, Lukas, Yayla, Mikail
Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the ultra low-pow
Externí odkaz:
http://arxiv.org/abs/2002.00909
Autor:
Günzel, Mario, Chen, Jian-Jia
During the execution of a job, it may suspend itself, i.e., its computation ceases to process until certain activities are complete to be resumed. This paper provides a counterexample of the schedulability analysis by Devi in Euromicro Conference on
Externí odkaz:
http://arxiv.org/abs/2001.05747