Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Database

Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System. Moreover, the Dataset encompasses the parallel execution of three different tasks that explores memory and CPU bound behaviors; Thus, the dataset also accounts for the parallel impact of shared resource contention between tasks. Other than the Performance counters, the Tasks and CPU Utilization are also sampled every monitoring period. This data-set focuses on six performance counters, namely Bus Access for Memory write operations, Stalls due to Write Buffer Full, L2D Writeback, Immediate Branches, CPU Cycle Count, and L1 Cache Hits, which are sampled in different levels of CPU Frequency for the same task-set, where every sample is coupled with the average utilization of the same task in a lower frequency level. A full description of the task-set and monitoring methodology can be found in the paper of the same name, published in IEEE Transactions on Computers.