2011 International Conference on Reconfigurable Computing and FPGAs 2011
DOI: 10.1109/reconfig.2011.59
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Measuring and Predicting Temperature Distributions on FPGAs at Run-Time

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Cited by 36 publications
(21 citation statements)
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“…The autonomous compute node achieves this by instantiating monitoring cores in the reconfigurable hardware that capture information about the system state, as for example thread-level performance and cache usage statistics. We also monitor the on-chip temperature distribution and use an adaptive temperature model [8] to predict future heat generation and heat flow. This enables us to avoid thermal hot-spots by proactive temperature-aware thread scheduling.…”
Section: Self-aware Hardware Software Platformmentioning
confidence: 99%
“…The autonomous compute node achieves this by instantiating monitoring cores in the reconfigurable hardware that capture information about the system state, as for example thread-level performance and cache usage statistics. We also monitor the on-chip temperature distribution and use an adaptive temperature model [8] to predict future heat generation and heat flow. This enables us to avoid thermal hot-spots by proactive temperature-aware thread scheduling.…”
Section: Self-aware Hardware Software Platformmentioning
confidence: 99%
“…In our previous work [7] we proposed self-calibrating temperature sensors based on ROs. The system contains an array of temperature sensors and calibrates them using an internal pre-calibrated thermal diode and internal regional heat-generating cores on a Virtex-6 FPGA.…”
Section: Related Workmentioning
confidence: 99%
“…Similar to [6,7,8] we use a RO combined with a timebase counter and a capture counter as temperature sensor. Furthermore, we access the temperature readings of our sen- Fig.…”
Section: Related Workmentioning
confidence: 99%
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“…Only a few attempts to exploit Dynamic Partial Reconfiguration (DPR) available in several FPGA architectures to design adhoc countermeasures against TDFs have been proposed [Di Carlo et al 2012;Kameda et al 2012].…”
Section: Introductionmentioning
confidence: 99%