Online concurrent workload classification for multi-core energy management

Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (V
_-f
). Such processors often need to execute applications concurrently, exhibiting varying and mixed workloads (e.g. compute- and memory-intensive) depending on the instruction mix and resource sharing. Runtime adaptation is key to achieving energy savings without trading-off application performance with such workload variabilities. In this paper, we propose an online energy management technique that performs concurrent workload classification using the metric Memory Reads Per Instruction (MRPI) and pro-actively selects an appropriate V
_-f
setting through workload prediction. Subsequently, it monitors the workload prediction error and performance loss, quantified by Instructions Per Second (IPS) at runtime and adjusts the chosen V
_-f
to compensate. We validate the proposed technique on an Odroid-XU3 with various combinations of benchmark applications. Results show an improvement in energy efficiency of up to 69% compared to existing approaches.