Ponències/Comunicacions de congressos
Recent Submissions
-
Accurate ILP-Based Contention Modeling on Statically Scheduled Multicore Systems
(IEEE, 2019-06-24)
Conference lecture
Open AccessCommercially available Off The Shelf (COTS) multicores have been assessed as the baseline computing platform even in the most conservative real-time domains. Multicore contention arising on shared hardware resources, with ... -
ePAPI: Performance Application ProgrammingInterface for Embedded Platforms
(2019)
Conference lecture
Open AccessPerformance Monitoring Counters (PMCs) have been traditionally used in the mainstream computing domain to perform debugging and optimization of software performance. PMCs are increasingly considered in embedded time-critical ... -
Generating and Exploiting Deep Learning Variants to Increase Heterogeneous Resource Utilization in the NVIDIA Xavier
(2019)
Conference lecture
Open AccessDeep learning-based solutions and, in particular, deep neural networks (DNNs) are at the heart of several functionalities in critical-real time embedded systems (CRTES) from vision-based perception (object detection and ... -
NoCo: ILP-Based Worst-Case Contention Estimation for Mesh Real-Time Manycores
(IEEE, 2019-01-07)
Conference lecture
Open AccessManycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared ... -
Machine Learning-based Query Augmentation for SPARQL Endpoints
(2018)
Conference lecture
Open AccessLinked Data repositories have become a popular source of publicly-available data. Users accessing this data through SPARQL endpoints usually launch several restrictive yet similar consecutive queries, either to find the ... -
When and How to Apply Statistics, Machine Learning and Deep Learning Techniques
(IEEE, 2018-09-21)
Conference lecture
Open AccessMachine Learning has become 'commodity' in engineering and experimental sciences, as calculus and statistics did before. After the hype produced during the 00's, machine learning (statistical learning, neural networks, ... -
LAEC: Look-Ahead Error Correction Codes in Embedded Processors L1 Data Cache
(IEEE, 2019-05-16)
Conference lecture
Open AccessAs implementation technology shrinks, the presence of errors in cache memories is becoming an increasing issue in all computing domains. Critical systems, e.g. space and automotive, are specially exposed and susceptible ... -
Assessing the Adherence of an Industrial Autonomous Driving Framework to ISO 26262 Software Guidelines
(Association for Computing Machinery (ACM), 2019-06-06)
Conference lecture
Open AccessThe complexity and size of Autonomous Driving (AD) software are comparably higher than that of software implementing other (standard) functionalities in the car. To make things worse, a big fraction of AD software is not ... -
Approximating a Multi-Grid Solver
(IEEE, 2019-02-14)
Conference lecture
Open AccessMulti-grid methods are numerical algorithms used in parallel and distributed processing. The main idea of multigrid solvers is to speedup the convergence of an iterative method by reducing the problem to a coarser grid a ... -
Towards Ad Hoc Recovery for Soft Errors
(IEEE, 2018-12-06)
Conference lecture
Open AccessThe coming exascale era is a great opportunity for high performance computing (HPC) applications. However, high failure rates on these systems will hazard the successful completion of their execution. Bit-flip errors in ... -
Maximum-Contention Control Unit (MCCU): Resource Access Count and Contention Time Enforcement
(IEEE, 2019-04-16)
Conference lecture
Open AccessIn real-time systems, the techniques to derive bounds to the contention tasks can suffer in multicore build on resource quota monitoring and enforcement. Existing techniques track and bound the number of requests to hardware ... -
AURIX TC277 Multicore Contention Model Integration for Automotive Applications
(IEEE, 2019-05-16)
Conference lecture
Open AccessThe ability to produce early guaranteed performance (worst-case execution time) estimates for multicores, i.e. before software from different providers gets integrated onto the same critical system, is pivotal. This helps ...
RSS 1.0