Name
Abstract | ||
|---|---|---|
Modern embedded multi-core processors are organized as clusters of cores, where all cores in each cluster operate at a common Voltage-frequency (14). 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. |
Year | Venue | Field |
|---|---|---|
2018 | PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) | Energy management,Efficient energy use,Workload,Computer science,Parallel computing,Real-time computing,Memory management,Time–frequency analysis,Shared resource,Instructions per second,Multi-core processor |
DocType | ISSN | Citations |
Conference | 1530-1591 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Basireddy Karunakar Reddy | 1 | 30 | 6.51 |
| geoffrey merrett | 2 | 411 | 49.30 |
| B. M. Al-Hashimi | 3 | 246 | 15.05 |
| Amit Kumar Singh | 4 | 19 | 6.81 |