A Reliability-Oriented Machine Learning Strategy for Heterogeneous Multicore Application Mapping

We propose a methodology to transparently estimate near-optimal application mappings aiming at increasing the Mean Workload to Failure (MWTF) in heterogeneous multicore processors. For that, we leverage an Artificial Neural Network (ANN) capable of estimating the vulnerability factor of RISC-V cores at runtime, which allows for efficient and dynamic application-to-core mappings targeting better MWTF and MWTF/energy tradeoffs. Results show that our ANN-based mapping yields very close-to-optimal solutions, with a difference in MWTF of only 3% when compared to the optimal mapping. When compared to a homogeneous architecture composed of only big cores, heterogeneous architectures may provide improvement in MWTF of up to 20.5% while impacting 12.2% on performance.