Device-parameter estimation with on-chip variation sensors considering random variability

Shinkai,; Hashimoto, Masanori

doi:10.1109/aspdac.2011.5722274

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The estimation error in the worst-case scenario was 12.7% for all simulated cases. Compared with the ring-oscillator (RO) based sensor design in CMOS technology shown in [33], the worst-case estimation error is improved by 1.6X, and the total number of devices required in on-chip sensors is reduced by 40X.…”

Section: Discussionmentioning

confidence: 99%

Parameter variation sensing and estimation in nanoscale fabrics

Zhang

Rahman

Narayanan

et al. 2014

Journal of Parallel and Distributed Computing

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Section: Discussionmentioning

confidence: 99%

Parameter variation sensing and estimation in nanoscale fabrics

Zhang

Rahman

Narayanan

et al. 2014

Journal of Parallel and Distributed Computing

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“…To deal with the above fact, we have proposed a deviceparameter extraction method considering random variations explicitly and demonstrated that the proposed method can accurately estimate both of global and random variations [18]. This method is based on MLE (Maximum Likelihood Estimation) and extracts not only D2D parameter variations but also standard deviations of random variations.…”

Section: On-chip Variation Sensors For Device-parameter Extractionmentioning

confidence: 99%

“…Here, ΔG x denotes global variability, and ΔR x corresponds to random variation of parameter x. Please see [18] for details including sensor structures. Table I lists the averages of the absolute estimation errors of ΔG x from the given variations.…”

Section: On-chip Variation Sensors For Device-parameter Extractionmentioning

confidence: 99%

Adaptive performance compensation with on-chip variation monitoring

Hashimoto

Fuketa

2011

2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS)

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Abstract-This paper discusses adaptive performance control with two types of on-chip variation sensors. The first sensor aims to extract several device-parameters for performance adaptation from a set of on-chip ring-oscillators with different sensitivities to device-parameters, and the device-parameter decomposition is discussed. The second sensors, which are embedded into functional circuits, predict timing errors due to PVT variations and aging. By controlling circuit performance according to the sensor outputs, PVT worst-case design can be overcome and power dissipation can be reduced while satisfying performance requirements. Measurement results of a subthreshold adder on 65-nm test chips show that the adaptive speed control can compensate PVT variations and improve energy efficiency by up to 46% compared to the worst-case design and operation with guardbanding.

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“…In the prior research [8], [9], the condition number of a matrix is used as the metric. The condition number of a matrix M , cond(M ), is mathematically defined as follows [10], [11], T , Jacobian matrix J (ΔG x ) is expressed as follows.…”

Section: Objective Function For Pursuing a Combination Of Sensitmentioning

confidence: 99%

“…This reconfiguration capability helps reduce silicon area necessary for the sensor, since the necessary number of RO types is reduced from n to 1. Here, to be precise, the structure of sensitivity-reconfigurable RO itself is presented in [8], [9]. However, it is used as one of ROs that provide a specific sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Extracting device-parameter variations using a single sensitivity-configurable ring oscillator

Higuchi

Shinkai

Hashimoto

et al. 2013

2013 18th Ieee European Test Symposium (Ets)

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Abstract-The RO(Ring-Oscillator)-based sensor is one of easily-implementable variation sensors, but for decomposing the observed variability into multiple unique device-parameter variations, a large number of ROs with different structures and sensitivities to device-parameters is required. This paper proposes a scheme for sensing multiple device-parameter variations with just a single reconfigurable RO. This sensitivity-configurable RO has a number of configurations available and this property can be exploited for reducing sensor area while improving estimation accuracy through iterative estimation. To minimize the prospective error, the proposed estimation iterates: (1) selecting the best configuration that minimizes the prospective estimation error around the current estimates; and (2) updating the estimates with the selected configuration. This experiment was carried out assuming a 32-nm predictive technology model. Experimental results show that device-parameter extraction with a single RO is feasible and the error of the extracted parameters is reduced by 35 to 53% with the improved objective function and iterative estimation.

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Device-parameter estimation with on-chip variation sensors considering random variability

Cited by 5 publications

References 11 publications

Parameter variation sensing and estimation in nanoscale fabrics

Parameter variation sensing and estimation in nanoscale fabrics

Adaptive performance compensation with on-chip variation monitoring

Extracting device-parameter variations using a single sensitivity-configurable ring oscillator

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