Procedure hopping: a low overhead solution to mitigate variability in shared-L1 processor clusters
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design
Variation-tolerant OpenMP tasking on tightly-coupled processor clusters
Proceedings of the Conference on Design, Automation and Test in Europe
HW-SW integration for energy-efficient/variability-aware computing
Proceedings of the Conference on Design, Automation and Test in Europe
Workload and user experience-aware dynamic reliability management in multicore processors
Proceedings of the 50th Annual Design Automation Conference
Explicit Java control of low-power heterogeneous parallel processing in the ToucHMore project
Proceedings of the 11th International Workshop on Java Technologies for Real-time and Embedded Systems
| Hi-index | 14.98 |
Multimedia streaming applications running on next-generation parallel multiprocessor arrays in sub-45 nm technology face new challenges related to device and process variability, leading to performance and power variations across the cores. In this context, Quality of Service (QoS), as well as energy efficiency, could be severely impacted by variability. In this work, we propose a runtime variability-aware workload distribution technique for enhancing real-time predictability and energy efficiency based on an innovative Linear-Programming + Bin-Packing formulation which can be solved in linear time. We demonstrate our approach on the virtual prototype of a next-generation industrial multicore platform running representative multimedia applications. Experimental results confirm that our technique compensates variability, while improving energy-efficiency and minimizing deadline violations in presence of performance and power variations across the cores. The proposed policy can save up to 33 percent of energy with respect to the state-of-the-art policies and 65 percent of energy with respect to one variability-unaware task allocation policy while providing better QoS.