A Unified Framework for System-Level Design: Modeling and Performance Optimization of Scalable Networking Systems

Jung, Hwisung; Pedram, Massoud

doi:10.1109/isqed.2007.26

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…As a typical application, we execute TCP/IP-related tasks (e.g., TCP segmentation and checksum offloading [41]). We vary the workload by changing the packet size from 64 bytes (e.g., 338,000 packets/sec) to 1,025 bytes (e.g., 84,819 packet/sec) [42].…”

Section: A Experimental Setupmentioning

confidence: 99%

Supervised Learning Based Power Management for Multicore Processors

Jung

Pedram

2010

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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109

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-This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state from some readily available input features (such as the occupancy state of a global service queue) and then uses this predicted state to look up the optimal power management action (e.g., voltage-frequency setting) from a precomputed policy table. The motivation for utilizing supervised learning in the form of a Bayesian classifier is to reduce the overhead of the PM which has to repetitively determine and assign voltage-frequency settings for each processor core in the system. Experimental results demonstrate that the proposed supervised learning based power management technique ensures system-wide energy savings under rapidly and widely varying workloads.Index Terms -Bayesian classification, dynamic power management, machine learning, multi-processor system, supervised learning

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Section: A Experimental Setupmentioning

confidence: 99%

Supervised Learning Based Power Management for Multicore Processors

Jung

Pedram

2010

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Self Cite

109

View full text Add to dashboard Cite

show abstract

“…Figure 9 illustrates the mapping process from workloads to an optimal operating frequency, assuming that 0.1 ≤ the arrival rate ≤ 0.9. In this figure, the pre-characterized mapping table is achieved through extensive offline simulations during design time, considering performance characteristics of each FB provided by the user or application, in a similar way as [5] [14]. For example, when a power manager predicts the workload for the near future, an optimal frequency value for the next decision epoch is selected and provided to the DFA which will continuously change the operating frequency from its present value to the target value.…”

Section: Mapping Of Workloads To Optimal Frequencymentioning

confidence: 99%