XEEMU: An Improved XScale Power Simulator

Herczeg, Zoltán; Kiss, Ákos; Schmidt, Daniel; Wehn, Norbert; Gyimóthy, Tibor

doi:10.1007/978-3-540-74442-9_29

Cited by 16 publications

(6 citation statements)

References 11 publications

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“…The accuracy of purely theoretical models, however, is always doubtful, as was shown, e.g., in [18] and [7]. Often they do not only give wrong absolute numbers but also give wrong trends, which is even worse.…”

Section: Methodsmentioning

confidence: 94%

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DRAM power management and energy consumption

Schmidt

Wehn

2009

Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes

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Energy efficiency is key in embedded systems design. Besides the CPU, DRAM has been identified as one of the main contributors to energy consumption in such devices. Modern DRAMs also offer low power states to adapt to varying workload and increase energy efficency. A number of studies have investigated different DRAM energy management strategies and propose a very aggressive use of low power states. The weakness of all of these studies is the underlying power model which does not account for transition overheads, and the lack of experimental evidence. We implemented a hardware controlled DRAM power management unit in an XScale based evaluation board and accurately measured the effects on runtime and power consumption. We observed that aggressive power management will even increase the average power consumption, due to the fact that all JEDEC compatible DRAMs execute a refresh when entering the power saving SREF-mode. This is not reflected in current, published power models. Thus, they dramatically overestimate the effectiveness of using low power states. We developed a new model for accurate timing and energy simulation of the observed effects. This model is integrated into the XEEMU XScale Energy Emulator.

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Section: Methodsmentioning

confidence: 94%

“…Just like DRAMsim, also the freely available open source XScale Energy Emulator XEEMU [7] offers a cycle accurate simulation of the DRAM subsystem. The energy consumption estimation is also based on the Micron power model.…”

Section: Related Workmentioning

confidence: 99%

DRAM power management and energy consumption

Schmidt

Wehn

2009

Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes

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“…We would also like to expand the simulator models with more parameters such as (off-chip) memory, effectively allowing for System-on-Chip exploration. In order to overcome the aforementioned limitations of XTREM, we are considering the XEEMU [21] simulator as a candidate for the next phase of the framework.…”

Section: Discussionmentioning

confidence: 99%

ImpEDE: A multidimensional design-space exploration framework for biomedical-implant processors

Dave

Strydis

Gaydadjiev

2010

ASAP 2010 - 21st IEEE International Conference on Application-Specific Systems, Architectures and Processors

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Abstract-The demand for biomedical implants keeps increasing. However, most of the current implant design methodologies involve custom-ASIC design. The SiMS project aims to change this process and make implant design more modular, flexible, faster and extensible. The most recent work within the SiMS context provides ImpEDE, a framework based on a multiobjective genetic algorithm, for automatic exploration of the design space of implant processors. The framework provides the processor designer with a Pareto front through which informed decisions can be made about specific implant families after analyzing their particular tradeoffs and requirements. A highly efficient, parallelized version of the genetic algorithm is also used to evolve the front and has as its objectives the optimization of power, performance and area. In addition, we illustrate the extensibility of our framework by modifying it to include a case study of a synthetic implant application with hard realtime deadlines.

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“…To obtain the energy consumption values of the instructions and system functions, we performed energy profiling with 200 times simulations per an instruction. The profiling was carried out with the instruction-based energy estimation tools, EMSIM 2.0 [20] and XEEMU [21]. These tools give the energy values in joule for input codes based on their implicit hardware architecture.…”

Section: Figure 2 the Energy Profiling Procedures Of An Instructionmentioning

confidence: 99%