ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
Variation-Aware Application Scheduling and Power Management for Chip Multiprocessors
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
Within-Die Variation-Aware Scheduling in Superscalar Processors for Improved Throughput
IEEE Transactions on Computers
Proceedings of the 7th ACM international conference on Computing frontiers
Variability-tolerant workload allocation for MPSoC energy minimization under real-time constraints
ACM Transactions on Embedded Computing Systems (TECS)
Sliding-mode control to compensate PVT variations in dual core systems
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Within-die variation in leakage power consumption is substantial and increasing for chip-level multiprocessors (CMPs) and multiprocessor systems-on-chip. Dealing with this problem via conservative assumptions is sub-optimal. Instead, operating systems may adapt task assignment and power management decisions to the variable characteristics of cores, improving system-wide power consumption and performance. Researchers have proposed such adaptation techniques. However, they rely on knowledge of CMP process variation (PV) maps. These maps are not provided by processor vendors, providing them would impose additional cost during the testing process, and static maps would not permit adaptation to aging effects. Further progress on developing and validating PV aware control techniques for CMPs requires access to PV maps for real processors. We present an online technique to extract the PV maps of CMPs. Potentially automatic temperature measurements with built-in on-die sensors during the execution of characterization workloads are used to determine variation in leakage power consumption. The proposed technique is applied to real CMPs, and the resulting PV maps are used within a PV aware task assignment and scheduling algorithm.