A Machine Learning Approach for Reliability-Aware Application Mapping for Heterogeneous Multicores

We propose a transparent and runtime methodology to increase the system's Mean Workload to Failure (MWTF) in heterogeneous multicore processors. For that, we leverage an Artificial Neural Network that makes online predictions of the core's Architectural Vulnerability Factor (AVF), which allows for reliability-aware application-to-core mappings. We experiment with different configurations of RISC-V cores and compare the MWTF of prediction-based mappings against the optimal oracle, showing that our proposed model provides MWTF as close as 5.6% to the oracle. We also compare homogeneous and heterogeneous multicores, showing that heterogeneity provides room for increasing the MWTF in up to 19.4%.