SRAM Redundancy - Silicon Area versus Number of Repairs Trade-off

Bickford, Jeanne Paulette; Rosner, R.J.; Hedberg, Erik; Yoder, Jonilyn; Barnett, T.S.

doi:10.1109/asmc.2008.4529077

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…Regarding the core, we note that DVFS becomes harder to use due to the need for decreased voltage for energy savings and increased minimum operating voltage to tolerate process variations [Bickford et al 2008]. As a consequence, the acceptable voltage range narrows down in each technology generation.…”

Section: Putting It All Togethermentioning

confidence: 99%

Sensible Energy Accounting with Abstract Metering for Multicore Systems

Liu

Moretó

Abella

et al. 2015

ACM Trans. Archit. Code Optim.

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Chip multicore processors (CMPs) are the preferred processing platform across different domains such as data centers, real-time systems, and mobile devices. In all those domains, energy is arguably the most expensive resource in a computing system. Accurately quantifying energy usage in a multicore environment presents a challenge as well as an opportunity for optimization. Standard metering approaches are not capable of delivering consistent results with shared resources, since the same task with the same inputs may have different energy consumption based on the mix of co-running tasks. However, it is reasonable for data-center operators to charge on the basis of estimated energy usage rather than time since energy is more correlated with their actual cost. This article introduces the concept of Sensible Energy Accounting (SEA). For a task running in a multicore system, SEA accurately estimates the energy the task would have consumed running in isolation with a given fraction of the CMP shared resources. We explain the potential benefits of SEA in different domains and describe two hardware techniques to implement it for a shared last-level cache and on-core resources in SMT processors. Moreover, with SEA, an energy-aware scheduler can find a highly efficient on-chip resource assignment, reducing by up to 39% the total processor energy for a 4-core system.

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Section: Putting It All Togethermentioning

confidence: 99%

Sensible Energy Accounting with Abstract Metering for Multicore Systems

Liu

Moretó

Abella

et al. 2015

ACM Trans. Archit. Code Optim.

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“…In particular, smaller geometries suffer from process variations, which limit the minimum voltage that can be used [Bickford et al 2008]. On the other hand, power efficiency and heat dissipation push for lower operating voltages.…”

Section: Voltage and Temperature Aware Energy Meteringmentioning

confidence: 99%

Hardware support for accurate per-task energy metering in multicore systems

Liu

Moretó

Jiménez

et al. 2013

ACM Trans. Archit. Code Optim.

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Accurately determining the energy consumed by each task in a system will become of prominent importance in future multicore-based systems because it offers several benefits, including (i) better application energy/performance optimizations, (ii) improved energy-aware task scheduling, and (iii) energy-aware billing in data centers. Unfortunately, existing methods for energy metering in multicores fail to provide accurate energy estimates for each task when several tasks run simultaneously. This article makes a case for accurate Per-Task Energy Metering (PTEM) based on tracking the resource utilization and occupancy of each task. Different hardware implementations with different trade-offs between energy prediction accuracy and hardware-implementation complexity are proposed. Our evaluation shows that the energy consumed in a multicore by each task can be accurately measured. For a 32-core, 2-way, simultaneous multithreaded core setup, PTEM reduces the average accuracy error from more than 12% when our hardware support is not used to less than 4% when it is used. The maximum observed error for any task in the workload we used reduces from 58% down to 9% when our hardware support is used. ACM Reference Format:Liu, Q., Moreto, M., Jimenez, V., Abella, J., Cazorla, F. J., and Valero M. 2013. Hardware support for accurate per-task energy metering in multicore systems. ACM Trans.

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“…Although voltage and temperature parameters may impact energy consumption of PTEM, their variability is expected to decrease with technology scaling and the increasing number of cores per chip. In particular, smaller geometries suffer from process variations, which limit the minimum voltage that can be used [Bickford et al 2008]. On the other hand, power efficiency and heat dissipation push for lower operating voltages.…”

Section: Voltage and Temperature Aware Energy Meteringmentioning

confidence: 99%