2 research outputs found
Using machine learning techniques to evaluate multicore soft error reliability
Virtual platform frameworks have been extended
to allow earlier soft error analysis of more realistic multicore
systems (i.e., real software stacks, state-of-the-art ISAs). The
high observability and simulation performance of underlying
frameworks enable to generate and collect more error/failurerelated data, considering complex software stack configurations,
in a reasonable time. When dealing with sizeable failure-related
data sets obtained from multiple fault campaigns, it is essential to
filter out parameters (i.e., features) without a direct relationship
with the system soft error analysis. In this regard, this paper proposes the use of supervised and unsupervised machine learning
techniques, aiming to eliminate non-relevant information as well
as identify the correlation between fault injection results and
application and platform characteristics. This novel approach
provides engineers with appropriate means that able are able to
investigate new and more efficient fault mitigation techniques.
The underlying approach is validated with an extensive data set
gathered from more than 1.2 million fault injections, comprising
several benchmarks, a Linux OS and parallelization libraries
(e.g., MPI, OpenMP), as well as through a realistic automotive
case study
Exploiting memory allocations in clusterized many-core architectures
Power-efficient architectures have become the most important feature required for future embedded systems. Modern
designs, like those released on mobile devices, reveal that clusterization is the way to improve energy efficiency. However, such
architectures are still limited by the memory subsystem (i.e., memory latency problems). This work investigates an alternative
approach that exploits on-chip data locality to a large extent, through distributed shared memory systems that permit efficient
reuse of on-chip mapped data in clusterized many-core architectures. First, this work reviews the current literature on memory
allocations and explore the limitations of cluster-based many-core architectures. Then, several memory allocations are introduced
and benchmarked scalability, performance and energy-wise, compared to the conventional centralized shared memory solution to
reveal which memory allocation is the most appropriate for future mobile architectures. Our results show that distributed shared
memory allocations bring performance gains and opportunities to reduce energy consumption